CN101299799A - Image detection, repair method and image detection, repair apparatus - Google Patents

Abstract

The invention provides an image bad point detecting method, comprising: performing the buffer memory on at least three continuous images including an origin image, at least a former image and at least a latter image; determining the pixel point in the origin image respectively relative to the forward motion vector of the former image and the backward motion vector of the latter image; determining the pixel point in the origin image respectively relative to the forward motion compensation point of the former image and relative to the backward motion compensation point of the latter image; performing the bad point detection on the pixel point in the origin image according to the forward motion compensation point and the backward motion compensation point. The method considers the characteristic of the image gray scale and the motion vector on the time and the space, obtains the motion compensation point in the former image and the latter image corresponding to the pixel in the origin image according to the motion vector, thereby increasing the detection precision of the bad point or the bad ring, improving the reparative accuracy of the bad point or the bad ring in the image.

Description

Image detection, restorative procedure and image detection, prosthetic device

Technical field

The present invention relates to technical field of image processing, particularly image detection, restorative procedure and image detection, prosthetic device.

Background technology

Along with the continuous development of digital image technology, various digital images are handled or the shooting product has obtained popularizing widely.People are also more and more higher to the quality requirement of image simultaneously, therefore just need make it can satisfy vision, psychology and other requirement to image is analyzed, processed and handles, and are referred to as image processing.Image repair is an important content in the image processing, and its application surface is very extensive, can repair the details of losing in damaged paintings, photo, the recovery Streaming Media, as repairing badly point or the bad piece etc. in the image.

Below just the technology that in the prior art bad point and bad piece in the image is detected and repair is simply introduced:

In the prior art, judge whether a pixel is bad point, and this pixel is repaired mainly in the following ways, promptly in a picture frame, judge according to the pixel value of other pixel around this pixel whether this pixel is bad point, if this pixel is very big with the pixel value difference of other pixel all around, can think that then this pixel is a bad point, for example Si Zhou pixel all is a dim spot, and this pixel is a bright spot, so just can think that this pixel is a bad point.Simultaneously, with reference to this bad point (pixel) pixel value of other pixel on every side, calculate the pixel value of this evil idea point by weighted average or alternate manner it is repaired.

Yet above-mentioned this mode but can't effectively detect the bad piece that occurs in the image and repair, this mainly is because the gray scale of other pixel around the above-mentioned bad point of repair mode main reference to bad point is repaired, but for the bad piece in the image, form by a plurality of bad points often, other pixel around these bad points often also is a bad point, do not have effective reference value, therefore just can't effectively repair yet the bad piece in the image.

Yet bad piece is the problem that often runs in actual life, as because camera lens on dust or CCD (Charge Coupled Device, charge coupled device) dielectric material reason such as come off causes the piece of fault at random bright or dark in the video image etc. on the imaging sensor, therefore needs a kind of method and solves reparation to bad piece in the image.

Prior art has also proposed a kind of detection and restorative procedure of image, and this method is promptly applicable to the detection and the reparation of bad point, also applicable to the detection and the reparation of bad piece.This method is simply said and is judged with reference to the pixel value of the pairing pixel of this pixel in the two field picture of front and back exactly and repair.When going bad some detection, (whether x is bad some y) for example to detect pixel, then with reference to pixel in the prior image frame frame (x, pixel y) and in the two field picture frame of back (x, pixel value y) is judged, if the pixel value of itself and front and back two field picture corresponding points differs too big, think that then it is exactly a bad point.The method that the different and above-mentioned same picture frame of this method detects, this method also is applicable to the detection to bad piece, for example comprises three bad point (x in the bad piece ₁, y ₁), (x ₂, y ₂) and (x ₃, y ₃), to bad point (x ₁, y ₁) when repairing, at first need to find pixel (x in the prior image frame ₁, y ₁) pairing pixel value, and pixel (x in the two field picture of back ₁, y ₁) pairing pixel value, obtain bad point (x according to weighted average or alternate manner then ₁, y ₁) pixel value, it is repaired; In the same way to bad point (x ₂, y ₂) and (x ₃, y ₃) repair, thereby finish reparation to bad piece.

The shortcoming that prior art exists is: mention in the above-mentioned prior art with reference to do not consider to move the influence to image of the method for front and back two field picture, that is to say the pixel A in this two field picture ₁(x ₁, y ₁), in the next frame image, moved to pixel B ₂(x ₂, y ₂), and this moment prior art also with reference to the pixel A in the next frame image ₂(x ₁, y ₁) judgement pixel A ₁(x ₁, y ₁) whether be bad point, thus the detection mistake caused, and influence is to the accuracy of the reparation of image.

Summary of the invention

Purpose of the present invention is intended to solve at least one of above-mentioned technological deficiency, is prone to when solving dead pixel points of images in the prior art or bad piece especially and detects wrong technological deficiency, reaches the purpose of the accuracy that improves image repair.

For achieving the above object, one aspect of the present invention proposes a kind of dead pixel points of images detection method, may further comprise the steps: at least three frame continuous images are carried out buffer memory, comprise this two field picture, at least one prior image frame and at least one back two field picture; Determine that pixel is respectively with respect to the forward motion vector of described prior image frame with respect to the described backward motion vector of two field picture afterwards in described the two field picture; Determine that according to described forward motion vector and described backward motion vector pixel described in described the two field picture is respectively with respect to the preceding frame motion compensation point of described prior image frame with respect to the described back frame motion compensation point of two field picture afterwards; According to frame motion compensation point before described and described back frame motion compensation point pixel described in described the two field picture is gone bad some detection.

The present invention also proposes a kind of dead pixel points of images restorative procedure on the other hand, may further comprise the steps: at least three frame continuous images are carried out buffer memory, comprise this two field picture, at least one prior image frame and at least one back two field picture; Determine that pixel is respectively with respect to the forward motion vector of described prior image frame with respect to the described backward motion vector of two field picture afterwards in described the two field picture; Determine that according to described forward motion vector and described backward motion vector pixel described in described the two field picture is respectively with respect to the preceding frame motion compensation point of described prior image frame with respect to the described back frame motion compensation point of two field picture afterwards; Judge according to frame motion compensation point before described and described back frame motion compensation point whether described pixel is bad point; If bad point is then repaired described pixel.

The present invention also proposes the bad piece detection method of a kind of image on the other hand, may further comprise the steps: at least three frame continuous images are carried out buffer memory, comprise this two field picture, at least one prior image frame and at least one back two field picture; Described two field picture, described prior image frame and described back two field picture are carried out piecemeal; Determine that piecemeal in described the two field picture is respectively with respect to the forward block motion vector of described prior image frame and back to block motion vector with respect to described back two field picture; Determine counting of bad pixel in the described piecemeal according to described forward block motion vector and described back to block motion vector; Judge according to counting of bad pixel in the described piecemeal whether described piecemeal is bad piece, if the counting greater than predetermined threshold value of described bad pixel, then described piecemeal is a bad piece.

The present invention also proposes the bad piece restorative procedure of a kind of image on the other hand, may further comprise the steps: at least three frame continuous images are carried out buffer memory, comprise this two field picture, at least one prior image frame and at least one back two field picture; Described two field picture, described prior image frame and described back two field picture are carried out piecemeal; Determine that piecemeal in described the two field picture is respectively with respect to the forward block motion vector of described prior image frame and back to block motion vector with respect to described back two field picture; To block motion vector the pixel in the described piecemeal is gone bad some detection according to described forward block motion vector and described back; Respectively detected bad point in the described piecemeal is repaired.

The present invention also proposes a kind of dead pixel points of images checkout gear on the other hand, comprise that picture frame cache module, motion vector computation module, motion compensation point determination module and bad point detect module, described picture frame cache module, be used at least three frame continuous images are carried out buffer memory, comprise this two field picture, at least one prior image frame and at least one back two field picture; Described motion vector computation module is used for the image according to described picture frame cache module buffer memory, determines that pixel is respectively with respect to the forward motion vector of described prior image frame with respect to the described backward motion vector of two field picture afterwards in described the two field picture; Described motion compensation point determination module, the described forward motion vector and the described backward motion vector that are used for obtaining according to described motion vector computation module determine that pixel described in described the two field picture is respectively with respect to the preceding frame motion compensation point of described prior image frame with respect to the described back frame motion compensation point of two field picture afterwards; Described bad is detected module, and the described preceding frame motion compensation point and the described back frame motion compensation point that are used for determining according to described motion compensation point determination module are gone bad some detection to pixel described in described the two field picture.

The present invention also proposes a kind of dead pixel points of images prosthetic device on the other hand, comprise that picture frame cache module, motion vector computation module, motion compensation point determination module, bad point detect module and repair module, described picture frame cache module, be used at least three frame continuous images are carried out buffer memory, comprise this two field picture, at least one prior image frame and at least one back two field picture; Described motion vector computation module is used for the image according to described picture frame cache module buffer memory, determines that pixel is respectively with respect to the forward motion vector of described prior image frame with respect to the described backward motion vector of two field picture afterwards in described the two field picture; Described motion compensation point determination module, the described forward motion vector and the described backward motion vector that are used for obtaining according to described motion vector computation module determine that pixel described in described the two field picture is respectively with respect to the preceding frame motion compensation point of described prior image frame with respect to the described back frame motion compensation point of two field picture afterwards; Described bad is detected module, and the described preceding frame motion compensation point and the described back frame motion compensation point that are used for determining according to described motion compensation point determination module are gone bad some detection to pixel described in described the two field picture; Described reparation module is used for when described bad some detection module detects described pixel for bad point described pixel being repaired.

The present invention also proposes the bad piece checkout gear of a kind of image on the other hand, comprise picture frame cache module, piecemeal module, block motion vector computing module, bad count determination module and bad piece detection module, described picture frame cache module, be used at least three frame continuous images are carried out buffer memory, comprise this two field picture, at least one prior image frame and at least one back two field picture; Described piecemeal module is used for this two field picture of described picture frame cache module buffer memory, described prior image frame and described back two field picture are carried out piecemeal; Described block motion vector computing module, be used for determining described piecemeal module to the piecemeal of described two field picture respectively with respect to the forward block motion vector of described prior image frame and back with respect to described back two field picture to block motion vector; The described evil idea determination module of counting, counting of described bad pixel of piecemeal determined to block motion vector in the described forward block motion vector and the described back that are used for obtaining according to described block motion vector computing module; Described bad piece detection module is used for judging according to described evil idea the counting of bad pixel that determination module determines of counting whether described piecemeal is bad piece, if the counting greater than predetermined threshold value of described bad pixel, then described piecemeal is a bad piece.

The present invention also proposes a kind of bad block repair apparatus of image on the other hand, comprise that picture frame cache module, piecemeal module, block motion vector computing module, bad point detect module and repair module, described picture frame cache module, be used at least three frame continuous images are carried out buffer memory, comprise this two field picture, at least one prior image frame and at least one back two field picture; Described piecemeal module is used for described two field picture of described picture frame cache module buffer memory, described prior image frame and described back two field picture are carried out piecemeal; Described block motion vector computing module is used for determining that described two field picture piecemeal is respectively with respect to the forward block motion vector of described prior image frame and back to block motion vector with respect to described back two field picture; Described bad point detects module, and the described forward block motion vector and the described back that are used for obtaining according to described block motion vector computing module are gone bad some detection to block motion vector to each pixel of described piecemeal; Described reparation module is used for the bad point of the detected piecemeal of described bad some detection module is repaired.

As embodiments of the invention, taken into full account image in time with the epigraph gray scale characteristics relevant in space with motion vector, obtain the motion compensation point in the two field picture of pixel correspondence front and back in this two field picture according to motion vector, and according to the pixel after the motion compensation pixel of this two field picture is gone bad a little and to be detected, thereby improve the accuracy of detection of bad point or bad piece, the accuracy that bad point or bad piece are repaired in the improvement image.

Aspect that the present invention adds and advantage part in the following description provide, and part will become obviously from the following description, or recognize by practice of the present invention.

Description of drawings

Above-mentioned and/or additional aspect of the present invention and advantage are from obviously and easily understanding becoming the description of embodiment below in conjunction with accompanying drawing, wherein:

Fig. 1 is the flow chart of one embodiment of the invention dead pixel points of images detection method;

Fig. 2 is the flow chart of one embodiment of the invention by classification piece matching way computing block motion vector;

Fig. 3 judges for a kind of of one embodiment of the invention proposition whether pixel is the method flow diagram of bad point;

The concrete implementation flow chart of reparation vector median filtering mode that Fig. 4 proposes for one embodiment of the invention;

The filter template W that Fig. 5 proposes for one embodiment of the invention _zSchematic diagram;

Fig. 6 is the flow chart of the bad piece detection method of the image of one embodiment of the invention;

Fig. 7 is definite forward block motion vector and the flow chart of back to block motion vector of one embodiment of the invention;

Fig. 8 is the dead pixel points of images checkout gear structure chart of one embodiment of the invention;

Fig. 9 is the dead pixel points of images prosthetic device structure chart of one embodiment of the invention;

Figure 10 is the bad piece checkout gear of the image of an one embodiment of the invention structure chart;

Figure 11 is the bad block repair apparatus structure chart of the image of one embodiment of the invention.

Embodiment

Describe embodiments of the invention below in detail, the example of described embodiment is shown in the drawings, and wherein identical from start to finish or similar label is represented identical or similar elements or the element with identical or similar functions.Below by the embodiment that is described with reference to the drawings is exemplary, only is used to explain the present invention, and can not be interpreted as limitation of the present invention.

The present invention mainly is: according to the kinetic characteristic of image on room and time bad piece or bad point are detected, obtain the preceding frame motion compensation point and the back frame motion compensation point of pixel correspondence in this two field picture by motion vector, thereby just can detect bad piece or bad point more accurately according to frame motion compensation point before described and back frame motion compensation point.For example (x, y), (x, forward motion vector y) and backward motion vector just can obtain this pixel A (x according to described forward motion vector and backward motion vector to access this pixel A by calculating for the pixel A in this two field picture ₁, y ₁) preceding frame motion compensation point B (x in the corresponding prior image frame ₂, y ₂) and corresponding back two field picture in back frame motion compensation point C (x ₃, y ₃).That is to say that in this three two field picture image is frame motion compensation point B (x in the past ₂, y ₂) move to pixel A (x ₁, y ₁), again by pixel A (x ₁, y ₁) move to back frame motion compensation point C (x ₃, y ₃).Definite by to preceding frame motion compensation point B and back frame motion compensation point C just can judge accurately whether pixel A is bad point, and this method also is applicable to the detection to bad piece equally like this.As one embodiment of the present of invention, the present invention carries out piecemeal to this two field picture, prior image frame and back two field picture simultaneously, and calculate the block motion vector of each piecemeal in this two field picture with respect to prior image frame and back two field picture, conduct belongs to the motion vector of all pixels in this piecemeal with regard to the available block motion vector like this, thereby can reduce operand effectively.Wherein, preferably, the mode of employing classification piece coupling obtains the block motion vector of piecemeal, with revised block motion vector in subordinate's image as the initial value of block motion vector at the corresponding levels, like this by can guarantee the accuracy of the block motion vector that finally obtains in every grade of image to the correction of this grade block motion vector.

Below just the description by specific embodiment the present invention is introduced:

As described in Figure 1, flow chart for one embodiment of the invention dead pixel points of images detection method, preceding frame motion compensation point and back frame motion compensation point by relative prior image frame of pixel in definite this two field picture and back two field picture, can effectively detect this pixel, improve the accuracy that dead pixel points of images detects.This embodiment may further comprise the steps:

Step S101 carries out buffer memory at least three frame continuous images, comprises this two field picture, at least one prior image frame and at least one back two field picture.Wherein, the number of prior image frame and back two field picture is at least one, as selects for use a plurality of prior image frames and/or back two field picture then will help to improve the precision that bad point detects.Need to prove select for use prior image frame and the back two field picture number can be unequal.

Step S102 determines that pixel in this two field picture is respectively with respect to the forward motion vector of prior image frame with respect to the backward motion vector of back two field picture.

As an optimal way of this step, can carry out piecemeal to this two field picture, prior image frame and back two field picture, and determine that each piecemeal in this two field picture is respectively with respect to the forward block motion vector of prior image frame and back to block motion vector with respect to the back two field picture.So just can be with the forward block motion vector of piecemeal and back to block motion vector as forward motion vector that belongs to all pixels in this piecemeal and backward motion vector, so can reduce operand effectively.As the foregoing description mode more preferably, the block motion vector that obtains by classification piece coupling, begin to calculate the block motion vector of this grade from the lowermost level image, and the block motion vector that subordinate's image the is obtained initial value of higher level's image motion vector the most, until the block motion vector that obtains highest image, thereby further improve the accuracy of block motion vector by the mode of approaching step by step.More preferably, subordinate's image is as before the image block motion vector initial value at the corresponding levels in the above-described embodiments, it is judged, if this subordinate's image unreliable then it is revised, thereby the accuracy of the block motion vector that assurance finally obtains further improves the accuracy of bad some detection.

As shown in Figure 2, be the flow chart of one embodiment of the invention, may further comprise the steps by classification piece matching way computing block motion vector:

Step S201 carries out m filtering with low pass filter respectively to this two field picture, prior image frame and back two field picture, obtains this two field picture, prior image frame and the back two field picture of m level different resolution, and the high more then described image level of resolution is high more.Wherein, m is preferably 2-5 time, and more excellent is 3 times.

Step S202 carries out piecemeal to this two field picture, prior image frame and back two field picture under different resolution, wherein, the resolution of classification is big or small opposite with described piecemeal, and resolution is low more, and piecemeal is big more; Otherwise resolution is big more, and then piecemeal is more little.

Step S203 from this two field picture of lowermost level (lowest resolution), searches for best matching blocks to each piecemeal in this this two field picture in prior image frame and back two field picture.Wherein, the fast adoptable matching criterior of search optimum Match is that modes such as the absolute value difference of minimum average B configuration (MAD), sqrt, crossvariance or the equal value difference of piece are determined best matching blocks.

Step S204, according to the above-mentioned best matching blocks that searches determine each piecemeal in this two field picture of lowermost level the forward block motion vector and the back to block motion vector.

Step S205 judges whether the forward block motion vector of piecemeal in above-mentioned definite this two field picture of lowermost level and back be reliable to block motion vector.Wherein, as a specific embodiment, the present invention proposes a kind of reliable method that judges whether, be about to calculate the MAD value of corresponding rectangular block in forward motion vector and backward motion vector MAD value and the calculating front and back frame, judge according to following formula whether motion vector is reliable:

$\mathrm{MV}(x,y)=\left\{\begin{array}{cc}1& \mathrm{if}{\mathrm{MAD}}_b(x,y)>T_1\mathrm{and}{\mathrm{MAD}}_f(x,y)>T_1\mathrm{and}{\mathrm{MAD}}_{\mathrm{bf}}(x,y)<T_2\\ 0& \mathrm{else}\end{array}\right.---\left(1\right)$

In above-mentioned formula (1), 1 expression (x, y) the motion vector MV of piece place (x, y) unreliable, 0 expression motion vector MV (x, y) reliable, wherein, T ₁And T ₂Be respectively preset threshold value, T ₂＜T ₁, b represents reverse, f represents propulsion, the motion before and after bf represents between the frame.Preferably, T ₁Be 20, T ₂Be 10.Wherein, above-mentioned judgment criterion has been utilized the continuity of moving on the time shaft, has therefore reduced the sensitivity to noise to a certain extent.

Step S206 if judge that the block motion vector of this two field picture of lowermost level calculating is unreliable, then revises it; If judge reliably, then execution in step S207.For detected unreliable motion vector, then calculate the MAD of this piece, and this block motion vector is revised with the neighborhood block motion vector of minimum MAD correspondence with the neighborhood block motion vector.Modified motion vector is as the initial value of next stage piece coupling.Equation expression is:

$\mathrm{MV}(x,y)=\underset{\mathrm{MVnb}(x,y)\&Element;A}{\arg \min }{\mathrm{MAD}}_{\mathrm{MVnb}(x,y)}---\left(2\right)$

In above-mentioned formula (2), (x, y) (x, neighborhood block motion vector y), A are the neighborhood set to expression motion vector MV to MVnb, MAD _{MVnb (x, y)}Be motion vector MV (x, the absolute value difference of minimum average B configuration of neighborhood block motion vector y).

Step S207, with each piecemeal correspondence in this two field picture of lowermost level reliably or through the block motion vector revised block motion vector initial value as corresponding piecemeal in this two field picture of its higher level, calculate the block motion vector of this two field picture of higher level.Be specially: search for the best matching blocks of this grade at one in more among a small circle reliably or through the block motion vector revised according to what subordinate's image provided, and then determine the block motion vector of each piecemeal in this two field picture of this grade according to the best matching blocks that searches.

Step S208, whether the block motion vector of each piecemeal is reliable in this two field picture of this grade that obtains among the determining step S207.Judge whether that reliable method is identical with the method that step S205 is adopted in same this step.If judge reliably, then execution in step S210; If it is unreliable to judge, execution in step S209 then.

Step S209 if judge that the block motion vector of this this two field picture of level (non-lowermost level image) calculating is unreliable, then revises it.

Same the present invention has also proposed a kind of correcting mode, but different with the formula of step S206 correction here, its difference is to have added upper level motion vector initial value, and this motion vector correction formula is:

${\mathrm{MV}}_{i-1}(x,y)=(1-\mathrm{\&lambda;}){\mathrm{MV}}_i(x,y)+\mathrm{\&lambda;}\underset{{\mathrm{MVnb}}_{i-1}(x,y)\&Element;A}{\arg \min }{\mathrm{MAD}}_{{\mathrm{MVnb}}_{i-1}(x,y)}---\left(3\right)$

Wherein, MV _I-1(x y) is (x, y) the revised motion vector in piece place, MV at the corresponding levels _iBe that (x, y) the revised motion vector in piece place, λ are weights to upper level, weigh upper level motion vector shared proportion in the correction of this grade motion vector, MVnb _I-1Be that (x, the y) motion vector of piece place neighborhood, A are the neighborhood set to this level, MAD _{MVnb (x, y)}Be motion vector MV (x, the absolute value difference of minimum average B configuration of neighborhood block motion vector y).

Step S210 will judge the block motion vector initial value of the block motion vector of reliable or process step S209 correction as corresponding piecemeal in this two field picture of its higher level among the step S208, calculate the block motion vector of this two field picture of higher level.The forward block motion vector of repeating step S208-S210 piecemeal in drawing highest this two field picture and back are to block motion vector.

Step S211, with the forward block motion vector of piecemeal in highest this two field picture and back to block motion vector as piecemeal described in this two field picture in the forward motion vector and the backward motion vector of all pixels.

Step S103, the forward motion vector and the backward motion vector of each pixel in this two field picture that obtains according to step S102, determine pixel in this two field picture respectively with respect to the preceding frame motion compensation point of prior image frame, and with respect to the back frame motion compensation point of back two field picture.As according to as described in forward motion vector and backward motion vector obtain this pixel A (x ₁, y ₁) preceding frame motion compensation point B (x in the corresponding prior image frame ₂, y ₂) and corresponding back two field picture in back frame motion compensation point C (x ₃, y ₃).

Step S104 goes bad some detection according to frame motion compensation point before described and described back frame motion compensation point to pixel described in described the two field picture.The present invention proposes a species diversity sort method and go bad the method that a little detects, be specially according to preceding frame motion compensation point B (x ₂, y ₂) and back frame motion compensation point C (x ₃, y ₃), and preceding frame motion compensation point B (x ₂, y ₂) neighborhood and back frame motion compensation point C (x ₃, y ₃) pixel A (x in u pixel of neighborhood and this two field picture ₁, y ₁) correlation, judge this pixel A (x ₁, y ₁) whether be bad point.Wherein, optional preceding frame motion compensation point B (x ₂, y ₂) and back frame motion compensation point C (x ₃, y ₃) horizontal u the pixel (do not comprise back frame motion compensation point C) of neighborhood, or vertically conduct such as u pixel with reference to point.Wherein, u is 1-9, and preferred u is 2, also can select more reference point for use in order to improve the accuracy of judgement degree certainly.

As one embodiment of the present of invention, the present invention proposes and a kind ofly judge that whether pixel is the method for bad point, select to remove preceding frame motion compensation point B (x among this embodiment ₂, y ₂) and back frame motion compensation point C (x ₃, y ₃) outside horizontal 2 neighborhood territory pixel points as with reference to pixel.As shown in Figure 3, specifically may further comprise the steps:

Step S301 supposes It is n two field picture coordinate $\stackrel{\&RightArrow;}{r}=(x,y)$ Place's gray value, and definition vector

: vector wherein

Be frame motion compensation point B (x before comprising ₂, y ₂) and back frame motion compensation point C (x ₃, y ₃), with and the vector of neighborhood territory pixel point.

$P\left(\stackrel{\&RightArrow;}{r}\right)=[p_1\left(\stackrel{\&RightArrow;}{r}\right),p_2\left(\stackrel{\&RightArrow;}{r}\right),p_3\left(\stackrel{\&RightArrow;}{r}\right),p_4\left(\stackrel{\&RightArrow;}{r}\right),p_5\left(\stackrel{\&RightArrow;}{r}\right),p_6\left(\stackrel{\&RightArrow;}{r}\right)]$

In the following formula (4),

N-1 frame and n+1 two field picture have been comprised $\stackrel{\&RightArrow;}{r}=(x,y)$ If six pixels of neighborhood are at the n two field picture $\stackrel{\&RightArrow;}{r}=(x,y)$ The place is a spot, so

Will with In pixel between no correlation, gray scale difference value each other is bigger, on the contrary gray scale difference value can be smaller.

Step S302 averages preceding frame motion compensation vertex neighborhood and back frame motion compensation vertex neighborhood gray values of pixel points, obtains the grade average.Wherein, proposed a kind of method that obtains the grade average, be specially vector as one embodiment of the present of invention In element resequence according to gray value, obtain new vector

$S\left(\stackrel{\&RightArrow;}{r}\right)=[s_1\left(\stackrel{\&RightArrow;}{r}\right),s_2\left(\stackrel{\&RightArrow;}{r}\right),s_3\left(\stackrel{\&RightArrow;}{r}\right),s_4\left(\stackrel{\&RightArrow;}{r}\right),s_5\left(\stackrel{\&RightArrow;}{r}\right),s_6\left(\stackrel{\&RightArrow;}{r}\right)]---\left(5\right)$

Wherein, $s_1\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_2\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_3\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_4\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_5\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_6\left(\stackrel{\&RightArrow;}{r}\right),$ And define a grade average

$m\left(\stackrel{\&RightArrow;}{r}\right)=(s_3\left(\stackrel{\&RightArrow;}{r}\right)+s_4\left(\stackrel{\&RightArrow;}{r}\right))/2---\left(6\right)$

Step S303 determines the grade difference, and described grade difference is weighed the size of spot correlation on pixel and the time neighborhood, described grade difference $d\left(\stackrel{\&RightArrow;}{r}\right)=[d_1\left(\stackrel{\&RightArrow;}{r}\right),d_2\left(\stackrel{\&RightArrow;}{r}\right),d_3\left(\stackrel{\&RightArrow;}{r}\right)]$ Obtain by following formula:

$d_i\left(\stackrel{\&RightArrow;}{r}\right)=\left\{\begin{array}{cc}{S}_i\left(\stackrel{\&RightArrow;}{r}\right)-I_n\left(\stackrel{\&RightArrow;}{r}\right)& I_n\left(\stackrel{\&RightArrow;}{r}\right)\&le;m\left(\stackrel{\&RightArrow;}{r}\right)\\ I_n\left(\stackrel{\&RightArrow;}{r}\right)-S_{1-i}\left(\stackrel{\&RightArrow;}{r}\right)& I_n\left(\stackrel{\&RightArrow;}{r}\right)>m\left(\stackrel{\&RightArrow;}{r}\right)\end{array}\right.\&ForAll;i=\mathrm{1,2,3}---\left(7\right)$

Wherein, described

It is n two field picture coordinate $\stackrel{\&RightArrow;}{r}=(x,y)$ Place's gray value.

Step S304 judges the correlation of pixel and grade average in described the two field picture, if relevant, then this pixel is not a bad point; If uncorrelated, then this pixel is a bad point.Be specially according to the grade difference

Whether greater than threshold value T _iJudge whether this pixel is relevant with the grade average in described the two field picture: $d_i\left(\stackrel{\&RightArrow;}{r}\right)>T_i$ I=1,2,3, wherein, i=1,2,3, T ₁, T ₂And T ₃Be predetermined threshold, and T ₁＜T ₂＜T ₃Set up as arbitrary inequality in the above-mentioned formula, judge that then pixel is uncorrelated with the grade average in this two field picture, this pixel is a bad point.Wherein, preferably, T1 is 5, T2 is 10, T3 is 40.Wherein, because threshold value T1 weighs each pixel of vertex neighborhood to be detected and the immediate degree of measuring point pixel value to be checked, so threshold value T1 is a most important threshold value.

As one embodiment of the present of invention, the present invention also proposes a kind of restorative procedure of dead pixel points of images, at first utilizes the dead pixel points of images detection method of the foregoing description to determine not repeat them here bad point in the image; By vector median filtering mode, gradient diffusion repair mode or weighted average repair mode described bad point is repaired then.As one embodiment of the present of invention, the present invention proposes and repair the concrete implementation of vector median filtering mode, as shown in Figure 4, may further comprise the steps:

Step S401 transfers filter template W _z, the template W of this filter wherein _z(z=1,2,3,4,5) are at the n two field picture $\stackrel{\&RightArrow;}{r}=(x,y)$ Place definition, template figure as shown in Figure 5, wherein template is of a size of 3 * 3, black region is represented the pixel that will use in the filtering in the template.Certainly also can select the template of other sizes, or select the template of other corresponding black region.

Step S402 finds described template W according to described bad point _zLast corresponding pixel, and obtain

Described

Determine by following formula, $X_{W_z}\left(i\right)=\arg \underset{X_i\&Element;W_z}{\min }{s}_{W_z}\left(i\right),$ Wherein, $s_{W_z}\left(i\right)=\underset{j\&Element;W_z}{\mathrm{\&Sigma;}}d(i,j),$ D (i, j)=| X _i-X _j|, X _i, X _jBe the bad some template W of institute _zThe middle pairing pixel gray value of black region, (i j) is X to d _i, X _jBetween distance.

Step S403, definition set $\hat{X}=\{X_{W_z}:z=\mathrm{1,2,3,4,5}\},$ And according to described $\hat{X}=\{X_{W_z}:z=\mathrm{1,2,3,4,5}\}$ Calculate s (z), described $s\left(z\right)=\underset{j=1}{\overset{5}{\mathrm{\&Sigma;}}}d(z,j)$ z＝1，2，3，4，5。

Step S404 selects s (z) minimum value pairing

As the pixel value at described bad some place, as pass through formula ${\hat{X}}_n\left(\stackrel{\&RightArrow;}{r}\right)=\arg \underset{X_z\&Element;\hat{X}}{\min }s\left(z\right)$ Obtain.

The foregoing description has illustrated the detection and the restorative procedure of the present invention's bad point in to image, and the present invention equally also can be applicable to the detection of bad piece in the image and reparation, below will be described with detection and the reparation to bad piece of the mode of embodiment.

As shown in Figure 6, the flow chart for the bad piece detection method of the image of one embodiment of the invention may further comprise the steps:

Step S601 carries out buffer memory at least three frame continuous images, comprises this two field picture, at least one prior image frame and at least one back two field picture.Wherein, the number of prior image frame and back two field picture is at least one, as selects for use a plurality of prior image frames and/or back two field picture will help to improve the precision that bad point detects.Need to prove select for use prior image frame and the back two field picture number can be unequal.

Step S602 carries out m filtering with low pass filter respectively to this two field picture, prior image frame and back two field picture, obtains this two field picture, prior image frame and the back two field picture of m level different resolution, and the high more then described image level of resolution is high more.Wherein, m is preferably 2-5 time, and more excellent is 3 times.

Step S603 carries out piecemeal to this two field picture, prior image frame and back two field picture under different resolution, wherein, the resolution of classification is big or small opposite with described piecemeal, and resolution is low more, and piecemeal is big more; Otherwise resolution is big more, and then piecemeal is more little.

Step S604 determines that piecemeal in this two field picture is respectively with respect to the forward block motion vector of prior image frame and back to block motion vector with respect to the back two field picture.As shown in Figure 7, the flow chart for one embodiment of the invention computing block motion vector specifically may further comprise the steps:

Step S701 from this two field picture of lowermost level (lowest resolution), searches for best matching blocks to each piecemeal in this this two field picture in prior image frame and back two field picture.Wherein, the fast adoptable matching criterior of search optimum Match is the absolute value difference of minimum average B configuration (MAD), sqrt, crossvariance or the equal value difference of piece etc.

Step S702, according to the above-mentioned best matching blocks that searches determine each piecemeal in this two field picture of lowermost level the forward block motion vector and the back to block motion vector.

Step S703 judges whether the forward block motion vector of piecemeal in above-mentioned definite this two field picture of lowermost level and back be reliable to block motion vector.Wherein, as a specific embodiment, the present invention proposes a kind of reliable method that judges whether, be about to calculate the MAD value of corresponding rectangular block in forward motion vector and backward motion vector MAD value and the calculating front and back frame, judge according to following formula whether motion vector is reliable:

$\mathrm{MV}(x,y)=\left\{\begin{array}{cc}1& \mathrm{if}{\mathrm{MAD}}_b(x,y)>T_1\mathrm{and}{\mathrm{MAD}}_f(x,y)>T_1\mathrm{and}{\mathrm{MAD}}_{\mathrm{bf}}(x,y)<T_2\\ 0& \mathrm{else}\end{array}\right.---\left(1\right)$

In above-mentioned formula (1), 1 expression (x, y) the motion vector MV of piece place (x, y) unreliable, 0 expression motion vector MV (x, y) reliable, wherein, T ₁And T ₂Be respectively preset threshold value, T ₂＜T ₁, b represents reverse, f represents propulsion, the motion before and after bf represents between the frame.Preferably, T ₁Be 20, T ₂Be 10.Wherein above-mentioned judgment criterion has been utilized the continuity of moving on the time shaft, has therefore reduced the sensitivity to noise to a certain extent.

Step S704 if judge that the block motion vector of this two field picture of lowermost level calculating is unreliable, then revises it; If reliable, execution in step S207 then.For detected unreliable motion vector, then calculate the MAD of this piece, and this block motion vector is revised with the neighborhood block motion vector of minimum MAD correspondence with the neighborhood block motion vector.Modified motion vector is as the initial value of next stage piece coupling.Equation expression is:

$\mathrm{MV}(x,y)=\underset{\mathrm{MVnb}(x,y)\&Element;A}{\arg \min }{\mathrm{MAD}}_{\mathrm{MVnb}(x,y)}---\left(2\right)$

In above-mentioned formula (2), (x, y) (x, neighborhood block motion vector y), A are the neighborhood set to expression motion vector MV to MVnb, MAD _{MVnb (x, y)}Be motion vector MV (x, the absolute value difference of minimum average B configuration of neighborhood block motion vector y).

Step S705, with each piecemeal correspondence in this two field picture of lowermost level reliably or through the block motion vector revised block motion vector initial value as corresponding piecemeal in this two field picture of its higher level, calculate the block motion vector of its this two field picture of higher level.Be specially: search for the best matching blocks of this grade at one in more among a small circle reliably or through the block motion vector revised according to what subordinate's image provided, and then determine the block motion vector of each piecemeal in this two field picture of this grade according to the best matching blocks that searches.

Step S706, whether the block motion vector of each piecemeal is reliable in this two field picture of this grade that obtains among the determining step S705.Judge whether that reliable method is identical with the method that step S703 is adopted in same this step.If judge reliably, then execution in step S708; If it is unreliable to judge, execution in step S707 then.

Step S707 if judge that the block motion vector of this this two field picture of level (non-lowermost level image) calculating is unreliable, then revises it.

The correction formula of same this step is different with the formula that step S704 revises, and its difference is to have added upper level motion vector initial value, and this motion vector correction formula is:

${\mathrm{MV}}_{i-1}(x,y)=(1-\mathrm{\&lambda;}){\mathrm{MV}}_i(x,y)+\mathrm{\&lambda;}\underset{{\mathrm{MVnb}}_{i-1}(x,y)\&Element;A}{\arg \min }{\mathrm{MAD}}_{{\mathrm{MVnb}}_{i-1}(x,y)}---\left(3\right)$

Wherein, MV _I-1(x y) is (x, y) the revised motion vector in piece place, MV at the corresponding levels _iBe that (x, y) the revised motion vector in piece place, λ are weights to upper level, weigh upper level motion vector shared proportion in the correction of this grade motion vector, MVnb _I-1Be that (x, the y) motion vector of piece place neighborhood, A are the neighborhood set to this level, MAD _{MVb (x, y)}Be motion vector MV (x, the absolute value difference of minimum average B configuration of neighborhood block motion vector y).

Step S708 will judge the block motion vector initial value of the block motion vector of reliable or process step S707 correction as corresponding piecemeal in this two field picture of its higher level among the step S706, calculate the block motion vector of its this two field picture of higher level.The forward block motion vector of repeating step S706-S708 piecemeal in drawing highest this two field picture and back are to block motion vector.

Step S605 go bad a little detection to block motion vector to each pixel in the piecemeal according to the forward block motion vector of piecemeal and back, determines counting of bad pixel in the piecemeal.Be specially: determine each pixel in this piecemeal respectively with respect to the forward motion vector of prior image frame with respect to the backward motion vector of back two field picture according to the forward block motion vector of the piecemeal correspondence in this two field picture and back to block motion vector, before determining according to forward motion vector and backward motion vector again frame motion compensation point and after frame motion compensation point; Last again according to each pixel in the piecemeal, and corresponding described before frame motion compensation point and described after frame motion compensation point each pixel in the described piecemeal is gone bad some detection, determine counting of bad pixel in the described piecemeal.Whether wherein, detect pixel is that the method for going bad point can not repeat them here with reference to flow process shown in Figure 3.

Step S606 judges according to counting of bad pixel in the piecemeal whether piecemeal is bad piece, if the counting greater than predetermined threshold value of bad pixel, then this piecemeal is a bad piece.Wherein, predetermined threshold value needs the size decision according to piecemeal, and as one 3 * 3 piecemeal, then can establish threshold value is 2, promptly just thinks that if any 2 bad points this piecemeal is a bad piece in this piecemeal.If piecemeal is very big certainly, and 1 or 2 bad point is wherein only arranged,, so just can think that this piecemeal is not a bad piece for insignificant.

Same the present invention has also proposed the restorative procedure to bad piece, when promptly step S606 determines that this piecemeal is bad piece in the above-described embodiments, respectively each the bad point in this bad piece is repaired.Wherein, can repair bad point by vector median filtering mode, gradient diffusion repair mode or weighted average repair mode.Wherein, the vector median filtering mode does not repeat them here shown in Fig. 4 flow chart.

As shown in Figure 8, dead pixel points of images checkout gear structure chart for one embodiment of the invention, this device comprises that picture frame cache module 810, motion vector computation module 820, motion compensation point determination module 830 and bad point detect module 840, picture frame cache module 810 is used at least three frame continuous images are carried out buffer memory, comprises this two field picture, at least one prior image frame and at least one back two field picture; Motion vector computation module 820 is used for the image according to picture frame cache module 810 buffer memorys, determines pixel in this two field picture respectively with respect to the forward motion vector of prior image frame, and with respect to the backward motion vector of back two field picture; Motion compensation point determination module 830 is used for the forward motion vector that obtains according to motion vector computation module 820 and backward motion vector and determines that this two field picture pixel is respectively with respect to the preceding frame motion compensation point of prior image frame with respect to the back back frame motion compensation point of two field picture; The preceding frame motion compensation points that bad some detection module 840 is used for determining according to motion compensation point determination module 830 are gone bad a little this two field picture pixel with back frame motion compensation point and are detected.

Wherein, as an embodiment, motion vector computation module 820 comprises piecemeal submodule 821 and block motion vector calculating sub module 822, and piecemeal submodule 821 is used for described two field picture of picture frame cache module 810 buffer memorys, described prior image frame and described back two field picture are carried out piecemeal; Block motion vector calculating sub module 822 is used for determining this two field picture piecemeal respectively with respect to the forward block motion vector of prior image frame and back to block motion vector with respect to the back two field picture, and wherein the forward block motion vector of piecemeal and back can be used as described forward motion vector and the described backward motion vector that belongs to pixel in the piecemeal to block motion vector.

Wherein, motion vector computation module 820 also comprises classification submodule 823 in the above-described embodiments, is used for according to resolution this two field picture, prior image frame and back two field picture being carried out classification respectively, wherein, the high more then described image level of resolution is high more, and the resolution of classification is big or small opposite with piecemeal; Block motion vector calculating sub module 822 also be used for the forward block motion vector of subordinate's image piecemeal and back to block motion vector as image at the corresponding levels in the initial value of corresponding piecemeal, obtain piecemeal in the described image at the corresponding levels the forward block motion vector and the back to block motion vector, in drawing highest image the forward block motion vector of piecemeal and the back to block motion vector.

Wherein, the motion vector computation module 820 in the foregoing description also comprises to be judged submodule 824 and revises submodule 825, judges that submodule 824 is used for judging whether the block motion vector of subordinate's image piecemeal is reliable; Revising submodule 825 is used for when judging that submodule 824 decision block motion vectors are unreliable this block motion vector being revised.

As shown in Figure 9, dead pixel points of images prosthetic device structure chart for one embodiment of the invention, this prosthetic device comprises that picture frame cache module 910, motion vector computation module 920, motion compensation point determination module 930, bad point detect module 940 and repair module 950, picture frame cache module 910 is used at least three frame continuous images are carried out buffer memory, comprises this two field picture, at least one prior image frame and at least one back two field picture; Motion vector computation module 920 is used for the image according to picture frame cache module 910 buffer memorys, determines that pixel in this two field picture is respectively with respect to the forward motion vector of prior image frame with respect to the backward motion vector of back two field picture; Motion compensation point determination module 930 is used for the forward motion vector that obtains according to motion vector computation module 920 and backward motion vector and determines that this two field picture pixel is respectively with respect to the preceding frame motion compensation point of prior image frame with respect to the back back frame motion compensation point of two field picture; The preceding frame motion compensation points that bad some detection module 940 is used for determining according to motion compensation point determination module 930 are gone bad a little this two field picture pixel with back frame motion compensation point and are detected; Repairing module 950 is used for when bad point detects module 940 detection pixels for bad point this pixel being repaired.

Wherein, as one embodiment of the present of invention, motion vector computation module 920 comprises piecemeal submodule 921 and block motion vector calculating sub module 922, and piecemeal submodule 921 is used for described two field picture of picture frame cache module 910 buffer memorys, described prior image frame and described back two field picture are carried out piecemeal; Block motion vector calculating sub module 922 is used for determining this two field picture piecemeal respectively with respect to the forward block motion vector of prior image frame and back to block motion vector with respect to the back two field picture, and wherein the forward block motion vector of piecemeal and back can be used as the forward motion vector and the backward motion vector of the described pixel that belongs to this piecemeal to block motion vector.

Wherein, motion vector computation module 920 also comprises classification submodule 923 in the above-described embodiments, is used for according to resolution this two field picture, prior image frame and back two field picture being carried out classification, wherein, the high more then image level of resolution is high more, and the resolution of classification is big or small opposite with piecemeal; Block motion vector calculating sub module 922 also be used for the forward block motion vector of subordinate's image piecemeal and back to block motion vector as image at the corresponding levels in the initial value of corresponding piecemeal, obtain piecemeal in the described image at the corresponding levels the forward block motion vector and the back to block motion vector, in drawing described highest image the forward block motion vector of piecemeal and the back to block motion vector.

Wherein, motion vector computation module 920 also comprises judgement submodule 924 and revises submodule 925 in the above-described embodiments, judges that submodule 924 is used for judging whether the block motion vector of subordinate's image piecemeal is reliable; Revising submodule 925 is used for when judging that submodule 924 judges that described block motion vector is unreliable this block motion vector being revised.

As shown in figure 10, be the bad piece checkout gear of the image of one embodiment of the invention structure chart, this bad piece checkout gear comprises picture frame cache module 1010, piecemeal module 1020, block motion vector computing module 1030, bad count determination module 1040 and bad piece detection module 1050, picture frame cache module 1010 is used at least three frame continuous images are carried out buffer memory, comprises this two field picture, at least one prior image frame and at least one back two field picture; Piecemeal module 1020 is used for this two field picture of picture frame cache module 1010 buffer memorys, prior image frame and back two field picture are carried out piecemeal; The piecemeal that block motion vector computing module 1030 is used for determining 1020 pairs of these two field pictures of piecemeal module is respectively with respect to the forward block motion vector of prior image frame and back to block motion vector with respect to the back two field picture; Counting of described bad pixel of piecemeal determined to block motion vector in forward block motion vector and back that the bad determination module 1040 of counting is used for obtaining according to block motion vector computing module 1030; Bad piece detection module 1050 is used for judging according to the counting of bad pixel that the bad determination module 1040 of counting is determined whether described piecemeal is bad piece, if the counting greater than predetermined threshold value of described bad pixel, then described piecemeal is a bad piece.

Wherein, as one embodiment of the present of invention, the bad determination module 1040 of counting comprises that motion compensation point determines that submodule 1041 and bad point detect submodule 1042, motion compensation point determine submodule 1041 be used for according to the forward block motion vector and after to block motion vector determine each pixel of piecemeal respectively with respect to the preceding frame motion compensation point of prior image frame and with respect to after the back frame motion compensation point of two field picture; Bad point detects submodule 1042 and is used for each pixel according to described piecemeal, and motion compensation point determine the correspondence that submodule 1041 obtains preceding frame motion compensation point and after frame motion compensation point each pixel in the piecemeal is gone bad some detection.

Wherein, block motion vector computing module 1030 also comprises classification submodule 1031 and block motion vector calculating sub module 1032 at the corresponding levels in the above-described embodiments, classification submodule 1031 is used for according to resolution this two field picture, prior image frame and back two field picture being carried out classification, wherein, the high more then described image level of resolution is high more, and the resolution of classification is big or small opposite with piecemeal; Block motion vector calculating sub module 1032 at the corresponding levels be used for the forward block motion vector of subordinate's image piecemeal and back to block motion vector as image at the corresponding levels in the initial value of corresponding piecemeal, obtain piecemeal in the image at the corresponding levels the forward block motion vector and the back to block motion vector, in drawing highest image the forward block motion vector of piecemeal and the back to block motion vector.

Wherein, block motion vector computing module 1030 also comprises judgement submodule 1033 and revises submodule 1034 in the above-described embodiments, judges that submodule 1033 is used for judging whether the block motion vector of subordinate's image piecemeal is reliable; Revising submodule 1034 is used for when judging that submodule 1033 decision block motion vectors are unreliable block motion vector being revised.

As shown in figure 11, be the bad block repair apparatus structure chart of the image of one embodiment of the invention, this bad block repair apparatus of image comprises that picture frame cache module 1110, piecemeal module 1120, block motion vector computing module 1130, bad point detect module 1140 and repair module 1150, picture frame cache module 1110 is used at least three frame continuous images are carried out buffer memory, comprises this two field picture, at least one prior image frame and at least one back two field picture; Piecemeal module 1120 is used for this two field picture of picture frame cache module 1110 buffer memorys, prior image frame and back two field picture are carried out piecemeal; Block motion vector computing module 1130 is used for determining that this two field picture piecemeal is respectively with respect to the forward block motion vector of prior image frame and back to block motion vector with respect to the back two field picture; The forward block motion vector that bad some detection module 1140 is used for obtaining according to block motion vector computing module 1130 is gone bad a little each pixel of piecemeal to block motion vector with the back and is detected; Repairing module 1150 is used for that bad point is detected the bad point that module 1140 detects piecemeal and repairs.

Wherein, as one embodiment of the present of invention, bad point detects a module 1140 and comprises that motion compensation point determines that submodule 1141 and bad point detect submodule 1142, motion compensation point determine submodule 1141 be used for according to the forward block motion vector and after to block motion vector determine each pixel of piecemeal respectively with respect to the preceding frame motion compensation point of prior image frame and with respect to after the back frame motion compensation point of two field picture; Bad point detects submodule 1142 and is used for each pixel according to piecemeal, and motion compensation point determine the correspondence that submodule 1141 obtains preceding frame motion compensation point and after frame motion compensation point each pixel in the piecemeal is gone bad some detection.

Wherein, block motion vector computing module 1130 also comprises classification submodule 1131 and block motion vector calculating sub module 1132 at the corresponding levels in the above-described embodiments, classification submodule 1131 is used for according to resolution this two field picture, prior image frame and back two field picture being carried out classification, wherein, the high more then image level of resolution is high more, and the resolution of classification is big or small opposite with piecemeal; Block motion vector calculating sub module 1132 at the corresponding levels be used for the forward block motion vector of subordinate's image piecemeal and back to block motion vector as image at the corresponding levels in the initial value of corresponding piecemeal, obtain piecemeal in the described image at the corresponding levels the forward block motion vector and the back to block motion vector, in drawing highest image the forward block motion vector of piecemeal and the back to block motion vector.

Wherein, block motion vector computing module 1130 also comprises judgement submodule 1133 and revises submodule 1134 in the above-described embodiments, judges that submodule 1131 is used for judging whether the block motion vector of subordinate's image piecemeal is reliable; Revising submodule 1134 is used for when judging that submodule 1133 decision block motion vectors are unreliable block motion vector being revised.

Advantage of the present invention is: taken into full account image in time with the epigraph gray scale characteristics relevant in space with motion vector, obtain the motion compensation point in the two field picture of pixel correspondence front and back in this two field picture according to motion vector, and according to the pixel after the motion compensation pixel of this two field picture is gone bad a little and to be detected, thereby improve the accuracy of detection of bad point or bad piece, the accuracy of the reparation of bad point or bad piece in the improvement image.

And the present invention represents the motion vector of each pixel in the piecemeal by block motion vector, thereby can effectively reduce operand.

The present invention also obtains piecemeal by the mode that adopts classification piece coupling block motion vector mates step by step, thereby has improved the accuracy that block motion vector calculates.

The present invention is also when the classification piece mates, reliability to the block motion vector of subordinate's image is judged, if it is unreliable then it is revised, thereby guarantee that block motion vector as higher level's image initial value all is reliably or through revising, thereby further improved the accuracy that block motion vector calculates.

Although illustrated and described embodiments of the invention, for the ordinary skill in the art, be appreciated that without departing from the principles and spirit of the present invention and can carry out multiple variation, modification, replacement and modification that scope of the present invention is by claims and be equal to and limit to these embodiment.

Claims (71)

1, a kind of dead pixel points of images detection method is characterized in that, may further comprise the steps:

At least three frame continuous images are carried out buffer memory, comprise this two field picture, at least one prior image frame and at least one back two field picture;

Determine that pixel is respectively with respect to the forward motion vector of described prior image frame with respect to the described backward motion vector of two field picture afterwards in described the two field picture;

Determine that according to described forward motion vector and described backward motion vector pixel described in described the two field picture is respectively with respect to the preceding frame motion compensation point of described prior image frame with respect to the described back frame motion compensation point of two field picture afterwards;

According to frame motion compensation point before described and described back frame motion compensation point pixel described in described the two field picture is gone bad some detection.

According to the described dead pixel points of images detection method of claim 1, it is characterized in that 2, pixel may further comprise the steps with respect to the forward motion vector of described prior image frame with respect to the described backward motion vector of two field picture afterwards respectively in described definite this two field picture:

Described two field picture, described prior image frame and described back two field picture are carried out piecemeal;

Determine that piecemeal in described the two field picture is respectively with respect to the forward block motion vector of described prior image frame and back to block motion vector with respect to described back two field picture;

With the forward block motion vector of described piecemeal with afterwards to described forward motion vector and the described backward motion vector of block motion vector as the pixel that belongs to described piecemeal.

3, according to the described dead pixel points of images detection method of claim 2, it is characterized in that, to this two field picture, prior image frame with before afterwards two field picture carries out piecemeal, also comprise described:

According to resolution described two field picture, described prior image frame and described back two field picture are carried out classification, wherein, the high more then described image level of resolution is high more, and the resolution of described classification and described piecemeal is big or small opposite;

The forward block motion vector of described definite described piecemeal and back specifically comprise to block motion vector:

Determine piecemeal in the highest image the forward block motion vector and the back to block motion vector.

According to the described dead pixel points of images detection method of claim 3, it is characterized in that 4, the forward block motion vector of piecemeal and back are determined by following steps to block motion vector in the described highest image:

The forward block motion vector of piecemeal and back are to block motion vector in the calculating lowermost level image;

Higher level's image from described lowermost level image, with the forward block motion vector of piecemeal in subordinate's image and back to block motion vector as image at the corresponding levels in the initial value of corresponding piecemeal, obtain piecemeal in the described image at the corresponding levels the forward block motion vector and the back to block motion vector, in drawing described highest image the forward block motion vector of piecemeal and the back to block motion vector.

According to the described dead pixel points of images detection method of claim 4, it is characterized in that 5, the forward block motion vector of piecemeal and back also comprise in obtaining subordinate's image after block motion vector:

Whether forward block motion vector and the back of judging piecemeal in the described subordinate image be reliable to block motion vector;

If judge reliably, then offer described image at the corresponding levels as forward block motion vector in the image at the corresponding levels and the initial value of back to block motion vector;

If it is unreliable to judge, then the forward block motion vector and the back of piecemeal in the described subordinate image are revised to block motion vector, revised forward block motion vector and back are offered image at the corresponding levels to block motion vector, as forward block motion vector in the image at the corresponding levels and the initial value of back to block motion vector.

6, according to each described dead pixel points of images detection method of claim 3 to 5, it is characterized in that piecemeal specifically comprises to block motion vector with respect to the forward block motion vector of described prior image frame with respect to described back the back of two field picture respectively in described definite this two field picture:

Determine the preceding frame best matching blocks of the corresponding described prior image frame of piecemeal in described the two field picture and the back frame best matching blocks of corresponding described back two field picture respectively;

Determine described forward block motion vector according to described piecemeal and described preceding frame best matching blocks, determine that according to described piecemeal and described back frame best matching blocks described back is to block motion vector.

7, according to the described dead pixel points of images detection method of claim 6, it is characterized in that, determine best matching blocks by piecemeal in described the two field picture is carried out the absolute value difference MAD of minimum average B configuration, sqrt, crossvariance or the equal value difference of piece.

According to the described dead pixel points of images detection method of claim 5, it is characterized in that 8, whether the described forward block motion vector of judging piecemeal in subordinate's image and back reliably specifically comprise to block motion vector: judge whether to satisfy MAD _b(x, y)＞T ₁, MAD _f(x, y)＞T ₁And MAD _Bf(x, y)＜T ₂, wherein, MAD _bThe absolute value difference of minimum average B configuration of expression reverse, MAD _fThe absolute value difference of minimum average B configuration of expression propulsion, MAD _BfThe absolute value difference of minimum average B configuration before and after the expression between the frame, T ₁, T ₂Be predetermined threshold value, and T ₂＜T ₁

If satisfy, think that then described block motion vector is unreliable;

If do not satisfy, think that then described block motion vector is reliable.

9, described according to Claim 8 dead pixel points of images detection method is characterized in that, described T ₁Be 20, described T ₂Be 10.

10, according to the described dead pixel points of images detection method of claim 7, it is characterized in that, block motion vector is revised further be may further comprise the steps:

Judge that whether described block motion vector is the block motion vector in the lowermost level image;

If then pass through formula $\mathrm{MV}(x,y)=\underset{\mathrm{MVnb}(x,y)\&Element;A}{\arg \min }{\mathrm{MAD}}_{\mathrm{MVnb}(x,y)}$ Described block motion vector is revised, and wherein, (x y) is block motion vector, MAD to MV _{MVnb (x, y)}(x, the absolute value difference of minimum average B configuration of neighborhood block motion vector y), A are the neighborhood set for motion vector MV;

If not, then pass through formula ${\mathrm{MV}}_{i-1}(x,y)=(1-\mathrm{\&lambda;}){\mathrm{MV}}_i(x,y)+\mathrm{\&lambda;}\underset{{\mathrm{MVnb}}_{i-1}(x,y)\&Element;A}{\arg \min }{\mathrm{MAD}}_{{\mathrm{MVnb}}_{i-1}(x,y)}$ Described block motion vector is revised, wherein MV (x y) is block motion vector, wherein, M4D _{MVnb (x, y)}Be motion vector MV (x, the absolute value difference of minimum average B configuration of neighborhood block motion vector y), MV _i(x is the revised motion vector in upper level image block place y), and λ is weights, MVnb _I-1Be the motion vector of this grade image block place neighborhood, A is the neighborhood set.

According to the described dead pixel points of images detection method of claim 1, it is characterized in that 11, preceding frame motion compensation point of described basis and back frame motion compensation point carry out bad spot check measuring tool body 4 to pixel in this two field picture and comprise:

According to frame motion compensation point before described and described back frame motion compensation point, and the correlation of pixel described in u pixel of frame motion compensation point and the described motion compensation of frame afterwards vertex neighborhood and described the two field picture before described, judge whether pixel described in described the two field picture is bad point.

According to the described dead pixel points of images detection method of claim 11, it is characterized in that 12, a described u pixel is 2 pixels,

Describedly judge that whether pixel described in this two field picture is that bad point further may further comprise the steps:

To frame motion compensation point before described and described back frame motion compensation point, 2 gray values of pixel points that reach described preceding frame motion compensation point and described back frame motion compensation vertex neighborhood average, and obtain the grade average;

Judge the correlation of pixel gray value described in described the two field picture and described grade average;

If relevant, then described pixel is not a bad point;

If uncorrelated, then described pixel is a bad point.

13, according to the described dead pixel points of images detection method of claim 12, it is characterized in that, described to preceding frame motion compensation point and described back frame motion compensation point, 2 gray values of pixel points that reach described preceding frame motion compensation point and described back frame motion compensation vertex neighborhood average, and obtain the grade average and are specially::

To frame motion compensation point before described and described back frame motion compensation point, 2 pixels that reach described preceding frame motion compensation point and described back frame motion compensation vertex neighborhood sort according to gray value, obtain vector $S\left(\stackrel{\&RightArrow;}{r}\right)=[s_1\left(\stackrel{\&RightArrow;}{r}\right),s_2\left(\stackrel{\&RightArrow;}{r}\right),s_3\left(\stackrel{\&RightArrow;}{r}\right),s_4\left(\stackrel{\&RightArrow;}{r}\right),s_5\left(\stackrel{\&RightArrow;}{r}\right),s_6\left(\stackrel{\&RightArrow;}{r}\right)],$ Wherein,

Pixel coordinate after the expression motion compensation, $s_1\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_2\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_3\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_4\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_5\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_6\left(\stackrel{\&RightArrow;}{r}\right);$

Described grade average For: $m\left(\stackrel{\&RightArrow;}{r}\right)=(s_3\left(\stackrel{\&RightArrow;}{r}\right)+s_4\left(\stackrel{\&RightArrow;}{r}\right))/2.$

According to the described dead pixel points of images detection method of claim 13, it is characterized in that 14, the correlation of pixel and grade average further may further comprise the steps in described this two field picture of judgement:

Determine the grade difference, described grade difference is weighed the size of spot correlation on pixel and the time neighborhood, described grade difference Obtain by following formula:

$d_i\left(\stackrel{\&RightArrow;}{r}\right)=\left\{\begin{array}{cc}{s}_i\left(\stackrel{\&RightArrow;}{r}\right)-I_n\left(\stackrel{\&RightArrow;}{r}\right)& I_n\left(\stackrel{\&RightArrow;}{r}\right)\&le;m\left(\stackrel{\&RightArrow;}{r}\right)\\ I_n\left(\stackrel{\&RightArrow;}{r}\right)-s_{l-i}\left(\stackrel{\&RightArrow;}{r}\right)& I_n\left(\stackrel{\&RightArrow;}{r}\right)>m\left(\stackrel{\&RightArrow;}{r}\right)\end{array}\right.\&ForAll;i=\mathrm{1,2,3}$ , wherein, described

It is n two field picture coordinate $\stackrel{\&RightArrow;}{r}=(x,y)$ Place's gray value,

According to described grade difference

Whether greater than threshold value T _iJudge whether pixel described in described the two field picture is relevant with described grade average: $d_i\left(\stackrel{\&RightArrow;}{r}\right)>T_i$ I=1,2,3, wherein, i=1,2,3, T ₁, T ₂And T ₃Be predetermined threshold, and T ₁＜T ₂＜T ₃

Set up as arbitrary inequality in the above-mentioned formula, judge that then pixel described in described the two field picture is uncorrelated with described grade average.

15, a kind of dead pixel points of images restorative procedure is characterized in that, may further comprise the steps:

At least three frame continuous images are carried out buffer memory, comprise this two field picture, at least one prior image frame and at least one back two field picture;

Determine that pixel is respectively with respect to the forward motion vector of described prior image frame with respect to the described backward motion vector of two field picture afterwards in described the two field picture;

Determine that according to described forward motion vector and described backward motion vector pixel described in described the two field picture is respectively with respect to the preceding frame motion compensation point of described prior image frame with respect to the described back frame motion compensation point of two field picture afterwards;

Judge according to frame motion compensation point before described and described back frame motion compensation point whether described pixel is bad point;

If bad point is then repaired described pixel.

According to the described dead pixel points of images restorative procedure of claim 15, it is characterized in that 16, pixel may further comprise the steps with respect to the forward motion vector of described prior image frame with respect to the described backward motion vector of two field picture afterwards respectively in described definite this two field picture:

Described two field picture, described prior image frame and described back two field picture are carried out piecemeal;

Determine that piecemeal in described the two field picture is respectively with respect to the forward block motion vector of described prior image frame and back to block motion vector with respect to described back two field picture;

With the forward block motion vector of described piecemeal with afterwards to described forward motion vector and the described backward motion vector of block motion vector as the described pixel that belongs to described piecemeal.

17, according to the described dead pixel points of images restorative procedure of claim 16, it is characterized in that, described this two field picture, described prior image frame and described back two field picture are carried out piecemeal before, also comprise:

According to resolution described two field picture, described prior image frame and described back two field picture are carried out classification, wherein, the high more then described image level of resolution is high more, and the resolution of described classification and described piecemeal is big or small opposite;

The forward block motion vector of described definite described piecemeal and back specifically comprise to block motion vector:

Determine piecemeal in the highest image the forward block motion vector and the back to block motion vector.

According to the described dead pixel points of images restorative procedure of claim 17, it is characterized in that 18, the forward block motion vector of piecemeal and back are determined by following steps to block motion vector in the described highest image:

The forward block motion vector of piecemeal and back are to block motion vector in the calculating lowermost level image;

Higher level's image from described lowermost level image, with the forward block motion vector of piecemeal in subordinate's image and back to block motion vector as image at the corresponding levels in the initial value of corresponding piecemeal, obtain piecemeal in the described image at the corresponding levels the forward block motion vector and the back to block motion vector, in drawing described highest image the forward block motion vector of piecemeal and the back to block motion vector.

According to the described dead pixel points of images restorative procedure of claim 18, it is characterized in that 19, the forward block motion vector of piecemeal and back also comprise in obtaining subordinate's image after block motion vector:

Whether forward block motion vector and the back of judging piecemeal in the described subordinate image be reliable to block motion vector;

If judge reliably, then offer image at the corresponding levels as forward block motion vector described in the image at the corresponding levels and the initial value of back to block motion vector;

If it is unreliable to judge, then the forward block motion vector and the back of piecemeal in the described subordinate image are revised to block motion vector, revised forward block motion vector and back are offered image at the corresponding levels to block motion vector, as forward block motion vector in the image at the corresponding levels and the initial value of back to block motion vector.

20, according to each described dead pixel points of images restorative procedure of claim 17 to 19, piecemeal specifically comprises to block motion vector with respect to the forward block motion vector of described prior image frame with respect to described back the back of two field picture respectively in described definite this two field picture:

Determine the preceding frame best matching blocks of the corresponding described prior image frame of piecemeal in described the two field picture and the back frame best matching blocks of corresponding described back two field picture respectively;

Determine described forward block motion vector according to described piecemeal and described preceding frame best matching blocks, determine that according to described piecemeal and described back frame best matching blocks described back is to block motion vector.

21, according to the described dead pixel points of images restorative procedure of claim 20, it is characterized in that, determine best matching blocks by piecemeal in described the two field picture is carried out the absolute value difference MAD of minimum average B configuration, sqrt, crossvariance or the equal value difference of piece.

According to the described dead pixel points of images restorative procedure of claim 19, it is characterized in that 22, whether the described forward block motion vector of judging piecemeal in subordinate's image and back reliably specifically comprise to block motion vector: judge whether to satisfy MAD _b(x, y)＞T ₁, MAD _f(x, y)＞T ₁And MAD _Bf(x, y)＜T ₂, wherein, MAD _bThe absolute value difference of minimum average B configuration of expression reverse, MAD _fThe absolute value difference of minimum average B configuration of expression propulsion, MAD _BfThe absolute value difference of minimum average B configuration before and after the expression between the frame, T ₁, T ₂Be predetermined threshold value, and T ₂＜T ₁

If satisfy, think that then described block motion vector is unreliable;

If do not satisfy, think that then described block motion vector is reliable.

23, according to the described dead pixel points of images restorative procedure of claim 19, it is characterized in that, block motion vector is revised further be may further comprise the steps:

Judge that whether described block motion vector is the block motion vector in the lowermost level image;

If then pass through formula $\mathrm{MV}(x,y)=\underset{\mathrm{MVnb}(x,y)\&Element;A}{\arg \min }{\mathrm{MAD}}_{\mathrm{MVnb}(x,y)}$ Described block motion vector is revised, and wherein, (x y) is block motion vector, MAD to MV _{MVnb (x, y)}(x, the absolute value difference of minimum average B configuration of neighborhood block motion vector y), A are the neighborhood set for motion vector MV;

If not, then pass through formula ${\mathrm{MV}}_{i-1}(x,y)=(1-\mathrm{\&lambda;}){\mathrm{MV}}_i(x,y)+\mathrm{\&lambda;}\underset{{\mathrm{MVnb}}_{i-1}(x,y)\&Element;A}{\arg \min }{\mathrm{MAD}}_{{\mathrm{MVnb}}_{i-1}(x,y)}$ Described block motion vector is revised, wherein MV (x y) is block motion vector, wherein, MAD _{MVnb (x, y)}Be motion vector MV (x, the absolute value difference of minimum average B configuration of neighborhood block motion vector y), MV _i(x is the revised motion vector in upper level image block place y), and λ is weights, MVnb _I-1Be the motion vector of this grade image block place neighborhood, A is the neighborhood set.

According to the described dead pixel points of images restorative procedure of claim 15, it is characterized in that 24, frame motion compensation point and back frame motion compensation point judge whether described pixel is that bad point specifically comprises before the described basis:

According to frame motion compensation point before described and described back frame motion compensation point, and the correlation of pixel described in u pixel of described preceding frame motion compensation point and described back frame motion compensation vertex neighborhood and this two field picture, judge whether pixel described in described the two field picture is bad point.

According to the described dead pixel points of images restorative procedure of claim 24, it is characterized in that 25, a described u pixel is 2 pixels,

Describedly judge that whether pixel described in this two field picture is that bad point further may further comprise the steps:

To frame motion compensation point before described and described back frame motion compensation point, 2 gray values of pixel points that reach described preceding frame motion compensation point and described back frame motion compensation vertex neighborhood average, and obtain the grade average;

Judge the correlation of pixel described in described the two field picture and described grade average;

If relevant, then described pixel is not a bad point;

If uncorrelated, then described pixel is a bad point.

26, according to the described dead pixel points of images restorative procedure of claim 25, it is characterized in that, to preceding frame motion compensation point and described back frame motion compensation point, and 2 gray values of pixel points of described preceding frame motion compensation point and described back frame motion compensation vertex neighborhood average, obtaining the grade average is specially: to frame motion compensation point before described and described back frame motion compensation point, 2 pixels that reach described preceding frame motion compensation point and described back frame motion compensation vertex neighborhood sort according to gray value, obtain vector $S\left(\stackrel{\&RightArrow;}{r}\right)=[s_1\left(\stackrel{\&RightArrow;}{r}\right),s_2\left(\stackrel{\&RightArrow;}{r}\right),s_3\left(\stackrel{\&RightArrow;}{r}\right),s_4\left(\stackrel{\&RightArrow;}{r}\right),s_5\left(\stackrel{\&RightArrow;}{r}\right),s_6\left(\stackrel{\&RightArrow;}{r}\right)],$ Wherein,

Pixel coordinate after the expression motion compensation, $s_1\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_2\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_3\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_4\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_5\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_6\left(\stackrel{\&RightArrow;}{r}\right);$

Described grade average For: $m\left(\stackrel{\&RightArrow;}{r}\right)=(s_3\left(\stackrel{\&RightArrow;}{r}\right)+s_4\left(\stackrel{\&RightArrow;}{r}\right))/2.$

According to the described dead pixel points of images restorative procedure of claim 26, it is characterized in that 27, the correlation of pixel and grade average further may further comprise the steps in described this two field picture of judgement:

Determine the grade difference, described grade difference is weighed the size of spot correlation on pixel and the time neighborhood, described grade difference

Obtain by following formula:

$d_i\left(\stackrel{\&RightArrow;}{r}\right)=\left\{\begin{array}{cc}{s}_i\left(\stackrel{\&RightArrow;}{r}\right)-I_n\left(\stackrel{\&RightArrow;}{r}\right)& I_n\left(\stackrel{\&RightArrow;}{r}\right)\&le;m\left(\stackrel{\&RightArrow;}{r}\right)\\ I_n\left(\stackrel{\&RightArrow;}{r}\right)-s_{l-i}\left(\stackrel{\&RightArrow;}{r}\right)& I_n\left(\stackrel{\&RightArrow;}{r}\right)>m\left(\stackrel{\&RightArrow;}{r}\right)\end{array}\right.\&ForAll;i=\mathrm{1,2,3}$ , wherein, described

It is n two field picture coordinate $\stackrel{\&RightArrow;}{r}=(x,y)$ Place's gray value,

According to described grade difference Whether greater than threshold value T _iJudge whether pixel described in described the two field picture is relevant with described grade average: $d_i\left(\stackrel{\&RightArrow;}{r}\right)>T_i$ I=1,2,3, wherein, i=1,2,3, T ₁, T ₂And T ₃Be predetermined threshold, and T ₁＜T ₂＜T ₃

Set up as arbitrary inequality in the above-mentioned formula, judge that then pixel described in this two field picture is uncorrelated with described grade average.

According to the described dead pixel points of images restorative procedure of claim 14, it is characterized in that 28, described bad point is repaired is specially:

By vector median filtering mode, gradient diffusion repair mode or weighted average repair mode described bad point is repaired.

According to the described dead pixel points of images restorative procedure of claim 28, it is characterized in that 29, described reparation vector median filtering mode may further comprise the steps:

Transfer filter template W _z

Find pixel corresponding on the described template Wz according to described bad point, and obtain , described

Determine by following formula, $X_{W_z}\left(i\right)=\arg \underset{X_i\&Element;W_z}{\min }{s}_{W_z}\left(i\right)$ , wherein, $s_{W_z}\left(i\right)=\underset{j\&Element;W_z}{\mathrm{\&Sigma;}}d(i,j),$ D (i, j)=| X _i-X _j|, X _i, X _jBe the bad some template W of institute _zThe middle pairing pixel gray value of black region, (i j) is X to d _i, X _jBetween distance;

Definition set $\hat{X}=\{X_{W_z}:z=\mathrm{1,2,3,4,5}\},$ And according to described $\hat{X}=\{X_{W_z}:z=\mathrm{1,2,3,4,5}\}$ Calculate s (z), described $s\left(z\right)=\underset{j=1}{\overset{5}{\mathrm{\&Sigma;}}}d(z,j)$ z＝1，2，3，4，5；

Select s (z) minimum value pairing

Pixel value as described bad some place.

30, the bad piece detection method of a kind of image is characterized in that, may further comprise the steps:

At least three frame continuous images are carried out buffer memory, comprise this two field picture, at least one prior image frame and at least one back two field picture;

Described two field picture, described prior image frame and described back two field picture are carried out piecemeal;

Determine that piecemeal in described the two field picture is respectively with respect to the forward block motion vector of described prior image frame and back to block motion vector with respect to described back two field picture;

Determine counting of bad pixel in the described piecemeal according to described forward block motion vector and described back to block motion vector;

Judge according to counting of bad pixel in the described piecemeal whether described piecemeal is bad piece, if counting greater than predetermined threshold value of described bad pixel judges that then described piecemeal is a bad piece.

31, according to the bad piece detection method of the described image of claim 30, it is characterized in that, described according to the forward block motion vector and the back determine that to block motion vector counting of bad pixel further comprises in the piecemeal:

Determine that to block motion vector each pixel is respectively with respect to the preceding frame motion compensation point of described prior image frame with respect to the described back frame motion compensation point of two field picture afterwards in the described piecemeal according to described forward block motion vector and described back;

According to each pixel in the described piecemeal, and frame motion compensation point and described frame motion compensation point are afterwards gone bad some detection to each pixel in the described piecemeal before each pixel correspondence described, determine counting of bad pixel in the described piecemeal.

32, according to the bad piece detection method of the described image of claim 30, it is characterized in that, described this two field picture, described prior image frame and described back two field picture are carried out piecemeal before, also comprise:

According to resolution described two field picture, described prior image frame and described back two field picture are carried out classification, wherein, the high more then described image level of resolution is high more, and the resolution of described classification and described piecemeal is big or small opposite;

The forward block motion vector of described definite described piecemeal and back specifically comprise to block motion vector:

Determine piecemeal in the highest image the forward block motion vector and the back to block motion vector.

According to the bad piece detection method of the described image of claim 32, it is characterized in that 33, the forward block motion vector of piecemeal and back are determined by following steps to block motion vector in the described highest image:

The forward block motion vector of piecemeal and back are to block motion vector in the calculating lowermost level image;

Higher level's image from described lowermost level image, with the forward block motion vector of piecemeal in subordinate's image and back to block motion vector as image at the corresponding levels in the initial value of corresponding piecemeal, obtain piecemeal in the described image at the corresponding levels the forward block motion vector and the back to block motion vector, in drawing described highest image the forward block motion vector of piecemeal and the back to block motion vector.

According to the bad piece detection method of the described image of claim 33, it is characterized in that 34, the forward block motion vector of piecemeal and back also comprise in obtaining subordinate's image after block motion vector:

Whether forward block motion vector and the back of judging piecemeal in the described subordinate image be reliable to block motion vector;

If judge reliably, then offer image at the corresponding levels as forward block motion vector in the image at the corresponding levels and the initial value of back to block motion vector;

If it is unreliable to judge, then the forward block motion vector and the back of piecemeal in the described subordinate image are revised to block motion vector, revised forward block motion vector and back are offered image at the corresponding levels to block motion vector, as forward block motion vector in the image at the corresponding levels and the initial value of back to block motion vector.

35, according to the bad piece detection method of each described image of claim 32 to 34, it is characterized in that, calculate the forward block motion vector and specifically comprise to block motion vector with the back:

Determine the preceding frame best matching blocks of the corresponding described prior image frame of piecemeal in described the two field picture and the back frame best matching blocks of corresponding described back two field picture respectively;

Determine described forward block motion vector according to described piecemeal and described preceding frame best matching blocks, determine that according to described piecemeal and described back frame best matching blocks described back is to block motion vector.

According to the bad piece detection method of the described image of claim 34, it is characterized in that 36, whether the described forward block motion vector of judging piecemeal in subordinate's image and back reliably specifically comprise to block motion vector: judge whether to satisfy MAD _b(x, y)＞T ₁, MAD _f(x, y)＞T ₁And MAD _Bf(x, y)＜T ₂, wherein, MAD _bThe absolute value difference of minimum average B configuration of expression reverse, MAD _fThe absolute value difference of minimum average B configuration of expression propulsion, MAD _BfThe absolute value difference of minimum average B configuration before and after the expression between the frame, T ₁, T ₂Be predetermined threshold value, and T ₂＜T ₁

If satisfy, think that then described block motion vector is unreliable;

If do not satisfy, think that then described block motion vector is reliable.

According to the bad piece detection method of the described image of claim 34, it is characterized in that 37, described block motion vector is revised further may further comprise the steps:

Judge that whether described block motion vector is the block motion vector in the lowermost level image;

If then pass through formula $\mathrm{MV}(x,y)=\underset{\mathrm{MVnb}(x,y)\&Element;A}{\arg \min }{\mathrm{MAD}}_{\mathrm{MVnb}(x,y)}$ Described block motion vector is revised, and wherein, (x y) is block motion vector, MAD to MV _{MVnb (x, y)}(x, the absolute value difference of minimum average B configuration of neighborhood block motion vector y), A are the neighborhood set for motion vector MV;

If not, then pass through formula ${\mathrm{MV}}_{i-1}(x,y)=(1-\mathrm{\&lambda;}){\mathrm{MV}}_i(x,y)+\mathrm{\&lambda;}\underset{{\mathrm{MVnb}}_{i-1}(x,y)\&Element;A}{\arg \min }{\mathrm{MAD}}_{{\mathrm{MVnb}}_{i-1}(x,y)}$ Described block motion vector is revised, wherein MV (x y) is block motion vector, wherein, MAD _{MVnb (x, y)}Be motion vector MV (x, the absolute value difference of minimum average B configuration of neighborhood block motion vector y), MV _i(x is the revised motion vector in upper level image block place y), and λ is weights, MVnb _I-1Be the motion vector of this grade image block place neighborhood, A is the neighborhood set.

38, according to the bad piece detection method of the described image of claim 31, it is characterized in that, describedly each pixel in the piecemeal is carried out bad spot check measuring tool body may further comprise the steps:

Preceding frame motion compensation point and back frame motion compensation point according to each the pixel correspondence in the piecemeal, and the correlation of pixel described in u pixel of described preceding frame motion compensation point and described back frame motion compensation vertex neighborhood and this two field picture, judge whether the pixel in the described piecemeal is bad point.

According to the bad piece detection method of the described image of claim 38, it is characterized in that 39, a described u pixel is 2 pixels,

Describedly judge that whether the pixel in the piecemeal is that bad point further may further comprise the steps:

To frame motion compensation point before described and described back frame motion compensation point, 2 gray values of pixel points that reach described preceding frame motion compensation point and described back frame motion compensation vertex neighborhood average, and obtain the grade average;

Judge the pixel in the described piecemeal and the correlation of described grade average;

If relevant, the pixel in the then described piecemeal is not a bad point;

If uncorrelated, the pixel in the then described piecemeal is a bad point.

40, according to the bad piece detection method of the described image of claim 39, it is characterized in that, to preceding frame motion compensation point and described back frame motion compensation point, and 2 gray values of pixel points of described preceding frame motion compensation point and described back frame motion compensation vertex neighborhood average, obtaining the grade average is specially: to frame motion compensation point before described and described back frame motion compensation point, 2 pixels that reach described preceding frame motion compensation point and described back frame motion compensation vertex neighborhood sort according to gray value, obtain vector $S\left(\stackrel{\&RightArrow;}{r}\right)=[s_1\left(\stackrel{\&RightArrow;}{r}\right),s_2\left(\stackrel{\&RightArrow;}{r}\right),s_3\left(\stackrel{\&RightArrow;}{r}\right),s_4\left(\stackrel{\&RightArrow;}{r}\right),s_5\left(\stackrel{\&RightArrow;}{r}\right),s_6\left(\stackrel{\&RightArrow;}{r}\right)],$ Wherein,

Pixel coordinate after the expression motion compensation,

$s_1\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_2\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_3\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_4\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_5\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_6\left(\stackrel{\&RightArrow;}{r}\right);$

Described grade average For: $m\left(\stackrel{\&RightArrow;}{r}\right)=(s_3\left(\stackrel{\&RightArrow;}{r}\right)+s_4\left(\stackrel{\&RightArrow;}{r}\right))/2.$

According to the bad piece detection method of the described image of claim 40, it is characterized in that 41, the pixel in the described judgement piecemeal and the correlation of grade average further may further comprise the steps:

Determine the grade difference, described grade difference is weighed the size of spot correlation on pixel and the time neighborhood, described grade difference

Obtain by following formula:

$d_i\left(\stackrel{\&RightArrow;}{r}\right)=\left\{\begin{array}{cc}{s}_i\left(\stackrel{\&RightArrow;}{r}\right)-I_n\left(\stackrel{\&RightArrow;}{r}\right)& I_n\left(\stackrel{\&RightArrow;}{r}\right)\&le;m\left(\stackrel{\&RightArrow;}{r}\right)\\ I_n\left(\stackrel{\&RightArrow;}{r}\right)-s_{l-i}\left(\stackrel{\&RightArrow;}{r}\right)& I_n\left(\stackrel{\&RightArrow;}{r}\right)>m\left(\stackrel{\&RightArrow;}{r}\right)\end{array}\right.\&ForAll;i=\mathrm{1,2,3}$ , wherein, described

It is n two field picture coordinate $\stackrel{\&RightArrow;}{r}=(x,y)$ Place's gray value,

According to described grade difference

Whether greater than threshold value T _iJudge whether the pixel in the described piecemeal is relevant with described grade average: $d_i\left(\stackrel{\&RightArrow;}{r}\right)>T_i$ I=1,2,3, wherein, i=1,2,3, T ₁, T ₂And T ₃Be predetermined threshold, and T ₁＜T ₂＜T ₃

Set up as arbitrary inequality in the above-mentioned formula, judge that then the pixel in the piecemeal is uncorrelated with described grade average.

42, the bad piece restorative procedure of a kind of image is characterized in that, may further comprise the steps:

At least three frame continuous images are carried out buffer memory, comprise this two field picture, at least one prior image frame and at least one back two field picture;

Described two field picture, described prior image frame and described back two field picture are carried out piecemeal;

Determine that piecemeal in described the two field picture is respectively with respect to the forward block motion vector of described prior image frame and back to block motion vector with respect to described back two field picture;

To block motion vector the pixel in the described piecemeal is gone bad some detection according to described forward block motion vector and described back;

Respectively detected bad point in the described piecemeal is repaired.

According to the bad piece restorative procedure of the described image of claim 42, it is characterized in that 43, described detection further comprises with afterwards going bad a little to block motion vector to the pixel in the described piecemeal according to the forward block motion vector:

Determine that to block motion vector each pixel is respectively with respect to the preceding frame motion compensation point of described prior image frame with respect to the described back frame motion compensation point of two field picture afterwards in the described piecemeal according to described forward block motion vector and described back;

According to each pixel in the described piecemeal, and frame motion compensation point and described frame motion compensation point are afterwards gone bad some detection to each pixel in the described piecemeal before each pixel correspondence described.

44, according to the bad piece restorative procedure of the described image of claim 43, it is characterized in that, described this two field picture, described prior image frame and described back two field picture are carried out piecemeal before, also comprise:

According to resolution described two field picture, described prior image frame and described back two field picture are carried out classification, wherein, the high more then described image level of resolution is high more, and the resolution of described classification and described piecemeal is big or small opposite;

The forward block motion vector of described definite described piecemeal and back specifically comprise to block motion vector:

Determine piecemeal in the highest image the forward block motion vector and the back to block motion vector.

According to the bad piece restorative procedure of the described image of claim 44, it is characterized in that 45, the forward block motion vector of piecemeal and back are determined by following steps to block motion vector in the described highest image:

The forward block motion vector of piecemeal and back are to block motion vector in the calculating lowermost level image;

Higher level's image from described lowermost level image, with the forward block motion vector of piecemeal in subordinate's image and back to block motion vector as image at the corresponding levels in the initial value of corresponding piecemeal, obtain piecemeal in the described image at the corresponding levels the forward block motion vector and the back to block motion vector, in drawing described highest image the forward block motion vector of piecemeal and the back to block motion vector.

According to the bad piece restorative procedure of the described image of claim 45, it is characterized in that 46, the forward block motion vector of piecemeal and back also comprise in obtaining subordinate's image after block motion vector:

Whether forward block motion vector and the back of judging piecemeal in the described subordinate image be reliable to block motion vector;

If judge reliably, then offer image at the corresponding levels as forward block motion vector in the image at the corresponding levels and the initial value of back to block motion vector;

If it is unreliable to judge, then the forward block motion vector and the back of piecemeal in the described subordinate image are revised to block motion vector, revised forward block motion vector and back are offered image at the corresponding levels to block motion vector, as forward block motion vector in the image at the corresponding levels and the initial value of back to block motion vector.

47, according to the bad piece restorative procedure of each described image of claim 44 to 46, it is characterized in that, calculate the forward block motion vector and specifically comprise to block motion vector with the back:

Determine the preceding frame best matching blocks of the corresponding described prior image frame of piecemeal in described the two field picture and the back frame best matching blocks of corresponding described back two field picture respectively;

Determine described forward block motion vector according to described piecemeal and described preceding frame best matching blocks, determine that according to described piecemeal and described back frame best matching blocks described back is to block motion vector.

According to the bad piece restorative procedure of the described image of claim 47, it is characterized in that 48, whether the described forward block motion vector of judging piecemeal in subordinate's image and back reliably specifically comprise to block motion vector: judge whether to satisfy MAD _b(x, y)＞T ₁, MAD _f(x, y)＞T ₁And MAD _Bf(x, y)＜T ₂, wherein, MAD _bThe absolute value difference of minimum average B configuration of expression reverse, MAD _fThe absolute value difference of minimum average B configuration of expression propulsion, MAD _BfThe absolute value difference of minimum average B configuration before and after the expression between the frame, T ₁, T ₂Be predetermined threshold value, and T ₂＜T ₁

If satisfy, think that then described block motion vector is unreliable;

If do not satisfy, think that then described block motion vector is reliable.

According to the bad piece restorative procedure of the described image of claim 46, it is characterized in that 49, described block motion vector is revised further may further comprise the steps:

Judge that whether described block motion vector is the block motion vector in the lowermost level image;

If then pass through formula $\mathrm{MV}(x,y)=\underset{\mathrm{MVnb}(x,y)\&Element;A}{\arg \min }{\mathrm{MAD}}_{\mathrm{MVnb}(x,y)}$ Described block motion vector is revised, and wherein, (x y) is block motion vector, MAD to MV _{MVnb (x, y)}(x, the absolute value difference of minimum average B configuration of neighborhood block motion vector y), A are the neighborhood set for motion vector MV;

If not, then pass through formula ${\mathrm{MV}}_{i-1}(x,y)=(1-\mathrm{\&lambda;}){\mathrm{MV}}_i(x,y)+\mathrm{\&lambda;}\underset{{\mathrm{MVnb}}_{i-1}(x,y)\&Element;A}{\arg \min }{\mathrm{MAD}}_{{\mathrm{MVnb}}_{i-1}(x,y)}$ Described block motion vector is revised, wherein MV (x y) is block motion vector, wherein, MAD _{MVnb (x, y)}Be motion vector MV (x, the absolute value difference of minimum average B configuration of neighborhood block motion vector y), MV _i(x is the revised motion vector in upper level image block place y), and λ is weights, MVnb _I-1Be the motion vector of this grade image block place neighborhood, A is the neighborhood set.

50, according to the bad piece restorative procedure of the described image of claim 43, it is characterized in that, described each pixel in the piecemeal is gone bad a little to detect may further comprise the steps:

Preceding frame motion compensation point and back frame motion compensation point according to each the pixel correspondence in the piecemeal, and the correlation of pixel described in u pixel of frame motion compensation point and the described motion compensation of frame afterwards vertex neighborhood and this two field picture before described, judge in the described piecemeal pixel whether be bad point.

According to the bad piece restorative procedure of the described image of claim 50, it is characterized in that 51, a described u pixel is 2 pixels,

Describedly judge that whether the pixel in the piecemeal is that bad point further may further comprise the steps:

To frame motion compensation point before described and described back frame motion compensation point, 2 gray values of pixel points that reach described preceding frame motion compensation point and described back frame motion compensation vertex neighborhood average, and obtain the grade average;

Judge the pixel in the described piecemeal and the correlation of described grade average;

If relevant, the pixel in the then described piecemeal is not a bad point;

If uncorrelated, the pixel in the then described piecemeal is a bad point.

52, according to the bad piece restorative procedure of the described image of claim 51, it is characterized in that, to preceding frame motion compensation point and described back frame motion compensation point, and 2 gray values of pixel points of described preceding frame motion compensation point and described back frame motion compensation vertex neighborhood average, obtaining the grade average is specially: to frame motion compensation point before described and described back frame motion compensation point, 2 pixels that reach described preceding frame motion compensation point and described back frame motion compensation vertex neighborhood sort according to gray value, obtain vector $S\left(\stackrel{\&RightArrow;}{r}\right)=[s_1\left(\stackrel{\&RightArrow;}{r}\right),s_2\left(\stackrel{\&RightArrow;}{r}\right),s_3\left(\stackrel{\&RightArrow;}{r}\right),s_4\left(\stackrel{\&RightArrow;}{r}\right),s_5\left(\stackrel{\&RightArrow;}{r}\right),s_6\left(\stackrel{\&RightArrow;}{r}\right)],$ Wherein,

Pixel coordinate after the expression motion compensation, $s_1\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_2\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_3\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_4\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_5\left(\stackrel{\&RightArrow;}{r}\right)\&le;s_6\left(\stackrel{\&RightArrow;}{r}\right);$

Described grade average

For: $m\left(\stackrel{\&RightArrow;}{r}\right)=(s_3\left(\stackrel{\&RightArrow;}{r}\right)+s_4\left(\stackrel{\&RightArrow;}{r}\right))/2.$

According to the bad piece restorative procedure of the described image of claim 52, it is characterized in that 53, the pixel in the described judgement piecemeal and the correlation of grade average further may further comprise the steps:

Determine the grade difference, described grade difference is weighed the size of spot correlation on pixel and the time neighborhood, described grade difference

Obtain by following formula:

$d_i\left(\stackrel{\&RightArrow;}{r}\right)=\left\{\begin{array}{cc}{s}_i\left(\stackrel{\&RightArrow;}{r}\right)-I_n\left(\stackrel{\&RightArrow;}{r}\right)& I_n\left(\stackrel{\&RightArrow;}{r}\right)\&le;m\left(\stackrel{\&RightArrow;}{r}\right)\\ I_n\left(\stackrel{\&RightArrow;}{r}\right)-s_{l-i}\left(\stackrel{\&RightArrow;}{r}\right)& I_n\left(\stackrel{\&RightArrow;}{r}\right)>m\left(\stackrel{\&RightArrow;}{r}\right)\end{array}\right.\&ForAll;i=\mathrm{1,2,3}$ , wherein, described

It is n two field picture coordinate $\stackrel{\&RightArrow;}{r}=(x,y)$ Place's gray value,

According to described grade difference

Whether greater than threshold value T _iJudge whether the pixel in the described piecemeal is relevant with described grade average: $d_i\left(\stackrel{\&RightArrow;}{r}\right)>T_i$ I=1,2,3, wherein, i=1,2,3, T ₁, T ₂And T ₃Be predetermined threshold, and T ₁＜T ₂＜T ₃

Set up as arbitrary inequality in the above-mentioned formula, judge that then the pixel in the piecemeal is uncorrelated with described grade average.

54, according to the bad piece restorative procedure of the described image of claim 42, it is characterized in that described detected bad point in the bad piece is repaired specifically comprises: by vector median filtering mode, gradient diffusion repair mode or weighted average repair mode described bad point is repaired.

According to the described dead pixel points of images restorative procedure of claim 54, it is characterized in that 55, described reparation vector median filtering mode may further comprise the steps:

Transfer filter template W _z

Find pixel corresponding on the described template Wz according to described bad point, and obtain , described

Determine by following formula, $X_{W_z}\left(i\right)\arg \underset{X_i\&Element;W_z}{\min }{s}_{W_z}\left(i\right)$ , wherein, $s_{W_z}\left(i\right)=\underset{j\&Element;W_z}{\mathrm{\&Sigma;}}d(i,j),$ D (i, j)=| X _i-X _j|, X _i, X _jBe the bad some template W of institute _zThe middle pairing pixel gray value of black region, (i j) is X to d _i, X _jBetween distance;

Definition set $\hat{X}=\{X_{W_z}:z=\mathrm{1,2,3,4,5}\},$ And according to described $\hat{X}=\{X_{W_z}:z=\mathrm{1,2,3,4,5}\}$ Calculate s (z), described $s\left(z\right)=\underset{j=1}{\overset{5}{\mathrm{\&Sigma;}}}d(z,j)$ z＝1，2，3，4，5；

Select s (z) minimum value pairing

Pixel value as described bad some place.

56, a kind of dead pixel points of images checkout gear is characterized in that, comprises that picture frame cache module, motion vector computation module, motion compensation point determination module and bad point detect module,

Described picture frame cache module is used at least three frame continuous images are carried out buffer memory, comprises this two field picture, at least one prior image frame and at least one back two field picture;

Described motion vector computation module is used for the image according to described picture frame cache module buffer memory, determines that pixel is respectively with respect to the forward motion vector of described prior image frame with respect to the described backward motion vector of two field picture afterwards in described the two field picture;

Described motion compensation point determination module, the described forward motion vector and the described backward motion vector that are used for obtaining according to described motion vector computation module determine that pixel described in described the two field picture is respectively with respect to the preceding frame motion compensation point of described prior image frame with respect to the described back frame motion compensation point of two field picture afterwards;

Described bad is detected module, and the described preceding frame motion compensation point and the described back frame motion compensation point that are used for determining according to described motion compensation point determination module are gone bad some detection to pixel described in described the two field picture.

According to the described dead pixel points of images checkout gear of claim 56, it is characterized in that 57, described motion vector computation module comprises piecemeal submodule and block motion vector calculating sub module,

Described piecemeal submodule is used for described two field picture of described picture frame cache module buffer memory, described prior image frame and described back two field picture are carried out piecemeal;

Described block motion vector calculating sub module, be used for determining described two field picture piecemeal respectively with respect to the forward block motion vector of described prior image frame and back to block motion vector with respect to described back two field picture, the forward block motion vector of wherein said piecemeal and back can be used as the described forward motion vector and the described backward motion vector of the described pixel that belongs to described piecemeal to block motion vector.

58, according to claim 56 or 57 described dead pixel points of images checkout gears, it is characterized in that, described motion vector computation module also comprises the classification submodule, be used for described two field picture, described prior image frame and described back two field picture being carried out classification according to resolution, wherein, the high more then described image level of resolution is high more, and the resolution of described classification is big or small opposite with described piecemeal;

Described block motion vector calculating sub module, also be used for the forward block motion vector of subordinate's image piecemeal and back to block motion vector as image at the corresponding levels in the initial value of corresponding piecemeal, obtain piecemeal in the described image at the corresponding levels the forward block motion vector and the back to block motion vector, in drawing described highest image the forward block motion vector of piecemeal and the back to block motion vector.

According to the described dead pixel points of images checkout gear of claim 57, it is characterized in that 59, described motion vector computation module also comprises to be judged submodule and revise submodule,

Described judgement submodule is used for judging whether the block motion vector of described subordinate image piecemeal is reliable;

Described correction submodule is used for when described judgement submodule judges that described block motion vector is unreliable described block motion vector being revised.

60, a kind of dead pixel points of images prosthetic device is characterized in that, comprises that picture frame cache module, motion vector computation module, motion compensation point determination module, bad point detect module and repair module,

Described picture frame cache module is used at least three frame continuous images are carried out buffer memory, comprises this two field picture, at least one prior image frame and at least one back two field picture;

Described motion vector computation module is used for the image according to described picture frame cache module buffer memory, determines that pixel is respectively with respect to the forward motion vector of described prior image frame with respect to the described backward motion vector of two field picture afterwards in described the two field picture;

Described motion compensation point determination module, the described forward motion vector and the described backward motion vector that are used for obtaining according to described motion vector computation module determine that pixel described in described the two field picture is respectively with respect to the preceding frame motion compensation point of described prior image frame with respect to the described back frame motion compensation point of two field picture afterwards;

Described bad is detected module, and the described preceding frame motion compensation point and the described back frame motion compensation point that are used for determining according to described motion compensation point determination module are gone bad some detection to pixel described in described the two field picture;

Described reparation module is used for when described bad some detection module detects described pixel for bad point described pixel being repaired.

According to the described dead pixel points of images prosthetic device of claim 60, it is characterized in that 61, described motion vector computation module comprises piecemeal submodule and block motion vector calculating sub module,

Described piecemeal submodule is used for described two field picture of described picture frame cache module buffer memory, described prior image frame and described back two field picture are carried out piecemeal;

Described block motion vector calculating sub module, be used for determining described two field picture piecemeal respectively with respect to the forward block motion vector of described prior image frame and back to block motion vector with respect to described back two field picture, the forward block motion vector of wherein said piecemeal and back can be used as the described forward motion vector and the described backward motion vector of the described pixel that belongs to described piecemeal to block motion vector.

62, according to claim 60 or 61 described dead pixel points of images prosthetic devices, it is characterized in that, described motion vector computation module also comprises the classification submodule, be used for described two field picture, described prior image frame and described back two field picture being carried out classification according to resolution, wherein, the high more then described image level of resolution is high more, and the resolution of described classification is big or small opposite with described piecemeal;

Described block motion vector calculating sub module, also be used for the forward block motion vector of subordinate's image piecemeal and back to block motion vector as image at the corresponding levels in the initial value of corresponding piecemeal, obtain piecemeal in the described image at the corresponding levels the forward block motion vector and the back to block motion vector, in drawing described highest image the forward block motion vector of piecemeal and the back to block motion vector.

According to the described dead pixel points of images prosthetic device of claim 61, it is characterized in that 63, described motion vector computation module also comprises to be judged submodule and revise submodule,

Described judgement submodule is used for judging whether the block motion vector of described subordinate image piecemeal is reliable;

Described correction submodule is used for when described judgement submodule judges that described block motion vector is unreliable described block motion vector being revised.

64, the bad piece checkout gear of a kind of image is characterized in that, comprises picture frame cache module, piecemeal module, block motion vector computing module, bad count determination module and bad piece detection module,

Described picture frame cache module is used at least three frame continuous images are carried out buffer memory, comprises this two field picture, at least one prior image frame and at least one back two field picture;

Described piecemeal module is used for this two field picture of described picture frame cache module buffer memory, described prior image frame and described back two field picture are carried out piecemeal;

Described block motion vector computing module, be used for determining described piecemeal module to the piecemeal of described two field picture respectively with respect to the forward block motion vector of described prior image frame and back with respect to described back two field picture to block motion vector;

The described evil idea determination module of counting, counting of described bad pixel of piecemeal determined to block motion vector in the described forward block motion vector and the described back that are used for obtaining according to described block motion vector computing module;

Described bad piece detection module is used for judging according to described evil idea the counting of bad pixel that determination module determines of counting whether described piecemeal is bad piece, if the counting greater than predetermined threshold value of described bad pixel, then described piecemeal is a bad piece.

According to the bad piece checkout gear of the described image of claim 64, it is characterized in that 65, the described evil idea determination module of counting comprises that motion compensation point determines that submodule and bad point detect submodule,

Described motion compensation point is determined submodule, is used for determining that to block motion vector each pixel of described piecemeal is respectively with respect to the preceding frame motion compensation point of described prior image frame with respect to the described back frame motion compensation point of two field picture afterwards according to described forward block motion vector and described back;

Described bad point detects submodule, be used for each pixel, and described motion compensation point determines that frame motion compensation point and the described point of frame motion compensation are afterwards gone bad some detection to each pixel in the described piecemeal before correspondence that submodule obtains described according to described piecemeal.

According to the bad piece checkout gear of the described image of claim 65, it is characterized in that 66, described block motion vector computing module also comprises classification submodule and block motion vector calculating sub module at the corresponding levels,

Described classification submodule is used for according to resolution described two field picture, described prior image frame and described back two field picture being carried out classification, and wherein, the high more then described image level of resolution is high more, and the resolution of described classification and described piecemeal is big or small opposite;

Described block motion vector calculating sub module at the corresponding levels, be used for the forward block motion vector of subordinate's image piecemeal and back to block motion vector as image at the corresponding levels in the initial value of corresponding piecemeal, obtain piecemeal in the described image at the corresponding levels the forward block motion vector and the back to block motion vector, in drawing described highest image the forward block motion vector of piecemeal and the back to block motion vector.

According to the bad piece checkout gear of the described image of claim 66, it is characterized in that 67, described block motion vector computing module also comprises to be judged submodule and revise submodule,

Described judgement submodule is used for judging whether the block motion vector of described subordinate image piecemeal is reliable;

Described correction submodule is used for when described judgement submodule judges that described block motion vector is unreliable described block motion vector being revised.

68, a kind of bad block repair apparatus of image is characterized in that, comprises that picture frame cache module, piecemeal module, block motion vector computing module, bad point detect module and repair module,

Described picture frame cache module is used at least three frame continuous images are carried out buffer memory, comprises this two field picture, at least one prior image frame and at least one back two field picture;

Described piecemeal module is used for described two field picture of described picture frame cache module buffer memory, described prior image frame and described back two field picture are carried out piecemeal;

Described block motion vector computing module is used for determining that described two field picture piecemeal is respectively with respect to the forward block motion vector of described prior image frame and back to block motion vector with respect to described back two field picture;

Described bad point detects module, and the described forward block motion vector and the described back that are used for obtaining according to described block motion vector computing module are gone bad some detection to block motion vector to each pixel of described piecemeal;

Described reparation module is used for the bad point of the detected described piecemeal of described bad some detection module is repaired.

According to bad block repair apparatus of the described image of claim 68, it is characterized in that 69, described bad point detects module and comprises that definite submodule of motion compensation point and bad point detect submodule,

Described motion compensation point is determined submodule, is used for determining that to block motion vector each pixel of described piecemeal is respectively with respect to the preceding frame motion compensation point of described prior image frame with respect to the described back frame motion compensation point of two field picture afterwards according to described forward block motion vector and described back;

Described bad point detects submodule, be used for each pixel, and described motion compensation point determines that frame motion compensation point and the described point of frame motion compensation are afterwards gone bad some detection to each pixel in the described piecemeal before correspondence that submodule obtains described according to described piecemeal.

According to bad block repair apparatus of the described image of claim 69, it is characterized in that 70, described block motion vector computing module also comprises classification submodule and block motion vector calculating sub module at the corresponding levels,

Described classification submodule is used for according to resolution described two field picture, described prior image frame and described back two field picture being carried out classification, and wherein, the high more then described image level of resolution is high more, and the resolution of described classification is big or small opposite with described piecemeal;

Described block motion vector calculating sub module at the corresponding levels, be used for the forward block motion vector of subordinate's image piecemeal and back to block motion vector as image at the corresponding levels in the initial value of corresponding piecemeal, obtain piecemeal in the described image at the corresponding levels the forward block motion vector and the back to block motion vector, in drawing described highest image the forward block motion vector of piecemeal and the back to block motion vector.

According to the bad piece checkout gear of the described image of claim 70, it is characterized in that 71, described block motion vector computing module also comprises to be judged submodule and revise submodule,

Described judgement submodule is used for judging whether the block motion vector of described subordinate image piecemeal is reliable;

Described correction submodule is used for when described judgement submodule judges that described block motion vector is unreliable described block motion vector being revised.

Images