CN111784733A - Image processing method, device, terminal and computer readable storage medium - Google Patents
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Abstract
The application discloses an image processing method, which is applied to the field of image processing and comprises the following steps: acquiring an image to be processed in real time, and determining a motion vector of the image to be processed through a preset search algorithm; if the motion vector is not 0 vector, processing the target type noise in the image to be processed into Gaussian distribution noise according to a preset transformation algorithm to obtain a noise processing image; filtering the noise processing image according to a preset filtering algorithm, and performing inverse transformation on the noise of the target type in the filtered image according to an inverse transformation algorithm of a preset transformation algorithm to obtain and output a first result image; and if the motion vector is a 0 vector, performing matching point weighted average calculation on the frame images of two adjacent frames before and after the current frame to obtain and output a second result image. The method also discloses an image processing device, a terminal and a computer readable storage medium, which can process the image in real time and improve the image definition.
Description
Technical Field
The present invention belongs to the field of image processing technologies, and in particular, to an image processing method, an image processing apparatus, a terminal, and a computer-readable storage medium.
Background
Dr (digital radiography) apparatus, i.e. direct digital radiography systems, are becoming increasingly popular with hospital radiology due to their important value in clinical assisted diagnosis. However, there are disadvantages, such as the poisson distribution type noise of the perspective image acquired by the flat panel detector is very obvious, which affects the clinical auxiliary diagnosis. Therefore, it is necessary to adopt a noise reduction algorithm to reduce image noise without affecting clinical diagnosis during image processing, where the noise reduction algorithm includes a gaussian filtering method, a bilateral filtering method, an NLM (Non-Local Means) filtering method, a BM3D (Block-matching and3 filtering) algorithm, and the like.
The image processing technology has a good effect of reducing and eliminating Gaussian noise, but the complete BM3D algorithm cannot process images in real time at present, and other algorithms can process images in real time, but have poor noise reduction effect on non-Gaussian noise. Therefore, the X-ray perspective images processed by the technology have low definition and cannot meet the requirement of clinical auxiliary diagnosis.
Disclosure of Invention
Embodiments of the present invention provide an image processing method, an image processing apparatus, a terminal, and a computer-readable storage medium, so as to solve the problems that an image cannot be processed in real time and the image processing quality is not high.
The embodiment of the invention provides an image processing method, which comprises the following steps:
acquiring an image to be processed in real time, and determining a motion vector of the image to be processed through a preset search algorithm; if the motion vector is not a 0 vector, processing the target type noise in the image to be processed into Gaussian distribution noise according to a preset transformation algorithm to obtain a noise processing image; filtering the noise processing image according to a preset filtering algorithm, and performing inverse transformation on the noise of the target type in the filtered image according to an inverse transformation algorithm of the preset transformation algorithm to obtain and output a first result image; and if the motion vector is a 0 vector, performing weighted average calculation on matching points of frame images of two adjacent frames before and after the current frame to obtain and output a second result image.
An embodiment of the present invention further provides an image processing apparatus, including:
the motion estimation module is used for acquiring an image to be processed in real time and determining a motion vector of the image to be processed through a preset search algorithm;
the noise transformation module is used for processing the target type noise in the image to be processed into Gaussian distribution noise according to a preset transformation algorithm if the motion vector is not a 0 vector, so as to obtain a noise processing image;
the filtering module is used for filtering the noise processing image according to a preset filtering algorithm; the noise transformation module is further configured to perform inverse transformation on the noise of the target type in the filtered image according to an inverse transformation algorithm of the preset transformation algorithm to obtain a first result image; an output module for outputting the first result image; and the calculating module is used for performing weighted average calculation on matching points of the frame images of the two adjacent frames before and after the current frame to obtain a second result image if the motion vector is a 0 vector. The output module is further configured to output the second result image.
An embodiment of the present invention further provides a terminal, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the image processing method as described above when executing the computer program.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the image processing method described above.
In the embodiment of the invention, an image to be processed is obtained in real time, a motion vector of the image to be processed is determined through a preset search algorithm, if the motion vector is not a 0 vector, noise of a target type in the image to be processed is processed into Gaussian distribution noise according to a preset conversion algorithm to obtain a noise processing image, the noise processing image is filtered according to a preset filtering algorithm, the noise of the target type in the filtered image is inversely transformed according to an inverse transformation algorithm of the preset conversion algorithm to obtain and output a first result image; if the motion vector is 0 vector, performing matching point weighted average calculation on the frame images of two adjacent frames before and after the current frame to obtain and output a second result image, searching the motion vector of the image first, and performing noise reduction processing in different modes respectively according to whether the motion vector is 0 vector, so that the noise reduction effect of the image can be improved, the processing speed is high, the real-time noise reduction of the image can be realized, and the information of the adjacent frames is fully utilized when the image is processed, so that the definition of the image can be further improved.
Drawings
Fig. 1 is a schematic flowchart of an image processing method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an application scenario of the image processing method of the present invention;
FIG. 3 is a flowchart illustrating an image processing method according to another embodiment of the present invention;
FIG. 4 is a schematic diagram of an image processing apparatus according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a terminal in an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a schematic flow chart of an image processing method according to an embodiment of the present invention, the image processing method can process a DR perspective image in real time and can also process other images, referring to fig. 2, fig. 2 is a schematic application scenario diagram of the image processing method, and a flat panel detector 10 and a terminal 20 are connected in a wired or wireless manner for data transmission. The flat panel detector 10 is used to acquire a perspective image, and the terminal 20 runs the image processing method to process the perspective image. The terminal 20 includes a PC or the like. The image processing method mainly comprises the following steps:
s101, acquiring an image to be processed in real time, and determining a motion vector of the image to be processed through a preset search algorithm;
the image processing method in this embodiment may be used to process the perspective image acquired by the flat panel detector in real time. And acquiring an image to be processed in real time, wherein the image to be processed is a perspective image acquired by the flat panel detector in real time.
The motion vector, i.e., the motion vector, represents the coordinates of the position of the motion-estimated point.
The preset search algorithm can be a full search method, a three-step search method, a four-step search method, a diamond search method and the like.
Judging whether the motion vector is a 0 vector, if not, indicating that the image content of the adjacent frame has relatively moved, and executing step S102; if yes, it is theoretically assumed that the image contents of the adjacent frames have not moved relatively, and step S104 is executed.
S102, if the motion vector is not 0 vector, processing the target type noise in the image to be processed into Gaussian distribution noise according to a preset transformation algorithm to obtain a noise processing image;
the target type of noise is additive noise, while the additive noise of flat panel detectors belongs to the poisson distribution.
Specifically, the additive noise in the image to be processed is transformed from poisson distribution to gaussian distribution noise according to a preset anscombe (anskomb) transformation algorithm, so as to obtain the noise processing image. The transformation relation is as follows:
wherein x is the pixel data of the image to be processed, and y is the pixel data of the noise processed image.
S103, filtering the noise processing image according to a preset filtering algorithm, and performing inverse transformation on the noise of the target type in the filtered image according to an inverse transformation algorithm of the preset transformation algorithm to obtain and output a first result image;
and filtering the noise processing image according to a preset filtering algorithm, converting additive noise in the filtered image from Gaussian distribution into Poisson distribution according to an inverse transformation algorithm of an anscombe transformation algorithm to obtain a first result image, and outputting the first result image, wherein the first result image can be specifically displayed on a screen of a terminal and provided for a user to view.
And S104, if the motion vector is a 0 vector, performing weighted average calculation on matching points of frame images of two adjacent frames before and after the current frame to obtain and output a second result image.
If the motion vector is a 0 vector, a reference pixel is determined in the current frame, and a matching point of the reference pixel is confirmed in the images of two adjacent frames before and after the current frame, wherein the matching point satisfies the minimum value of var (M2/M1), wherein M1 is the neighborhood of the reference pixel, M2 is the neighborhood of the matching point, and var represents the mean square error calculation. And then, obtaining a second result image through weighted average calculation of the matching points, and outputting the second result image, wherein the second result image can be displayed on a screen of a terminal and provided for a user to view.
In the embodiment of the invention, an image to be processed is obtained in real time, a motion vector of the image to be processed is determined through a preset search algorithm, if the motion vector is not a 0 vector, noise of a target type in the image to be processed is processed into Gaussian distribution noise according to a preset conversion algorithm to obtain a noise processing image, the noise processing image is filtered according to a preset filtering algorithm, the noise of the target type in the filtered image is inversely transformed according to an inverse transformation algorithm of the preset conversion algorithm to obtain and output a first result image; if the motion vector is 0 vector, performing matching point weighted average calculation on the frame images of two adjacent frames before and after the current frame to obtain and output a second result image, searching the motion vector of the image first, and performing noise reduction processing in different modes respectively according to whether the motion vector is 0 vector, so that the noise reduction effect of the image can be improved, the processing speed is high, the real-time noise reduction of the image can be realized, and the information of the adjacent frames is fully utilized when the image is processed, so that the definition of the image can be further improved.
Referring to fig. 3, fig. 3 provides an image processing method according to another embodiment of the present invention, including:
s201, acquiring an image to be processed in real time, and determining a motion vector of the image to be processed through a three-step search algorithm;
the three-step search method is a motion estimation algorithm, which mainly compares the center point of a square in a search area with eight search points around the square, calculates the SAD (Sum of absolute difference) value of the nine points, selects the point with the minimum SAD value as the center point of the next search, then uses the point obtained in the previous step as the center, reduces the step size of the current search to half of the step size of the previous search, then carries out similar search, tracks the minimum block error point, and thus, the best matching position can be found in the third search, which is the motion estimation point, and the coordinate is the motion vector.
Specifically, an image block of a preset size (e.g., 3 × 3) is taken as a reference block in the image of the current frame, and the reference block has 9 points in total, i.e., a center point and 8 surrounding points. Further, in two adjacent frames before and after the current frame, a pixel point which is the same as the center point of the reference block is used as an origin, an image block which takes the origin as the center point and has the same size as the reference block is used as an initial block, namely, the initial block is also formed by 9 points in total. The search block of the reference block is determined in the two adjacent frames with half of a preset maximum search length (e.g., 8) as a search step. Specifically, the pixel values of the corresponding positions of the reference block and the search block are subtracted to obtain absolute values, and the absolute values are summed to obtain the SAD value, so that when the SAD value is minimum, the block error is minimum, and the search block is most similar to the reference block. And taking a minimum block error point (MBD) corresponding to the minimum block error as a central point of the next step.
Further, the step length is halved, namely, the step length is reduced from 4 to 2, the center point moves to the MBD point of the previous step, and the step length is 2 points away from the new center point around the new center point again, and the comparison is carried out to obtain the center point of the next step. Then, the step size is reduced by half again, i.e. from 2 to 1, and the obtained MBD point is a motion estimation point, and its coordinates are a motion vector.
It is determined whether the motion vector is a 0 vector, i.e., whether the MBD point coordinates are (0,0), if yes, step S205 is executed, and if no, step S202 is executed.
S202, if the motion vector is not a 0 vector, converting additive noise in the image to be processed into Gaussian distribution noise according to an anscombe conversion algorithm to obtain a noise processing image;
s203, filtering the noise processing image according to a block matching filtering algorithm;
specifically, the filtering step includes: confirming a reference block in the noise processing image, and confirming similar blocks of a plurality of reference blocks in the noise processing image according to a preset matching rule;
integrating a plurality of similar blocks in the noise processing image into a three-dimensional matrix Q (P), and carrying out coefficient scaling on the three-dimensional matrix Q (P) through wiener filtering to realize filtering, wherein the scaling formula is as follows:
N(P)＝Twein_inverse(wp·Twein(Q1(P)))；
wherein, N (P) represents a coefficient matrix used as a coefficient when weighting; twein _ inverse () represents the three-dimensional inverse transform, wp being the wiener filter coefficient; twein (q (p)) represents a three-dimensional transformation of a three-dimensional matrix;
and transforming the three-dimensional matrix back to image estimation through three-dimensional inverse transformation, and restoring a plurality of similar blocks in the noise processing image to the original position of the noise processing image in a mode of weighting the value of the similar block at each corresponding position and the coefficient matrix N (P) to obtain the gray value of each pixel.
S204, performing inverse transformation on the additive noise in the filtered image according to an inverse transformation algorithm of an anscombe transformation algorithm, performing inverse transformation to obtain additive noise in the filtered image, converting the additive noise from Gaussian distribution back to Poisson distribution, and obtaining and outputting a first result image;
and S205, if the motion vector is a 0 vector, performing weighted average calculation on matching points of frame images of two adjacent frames before and after the current frame to obtain and output a second result image.
Specifically, a reference pixel Icur (x, y) is selected in the current frame, and a neighborhood of the reference pixel is determined, it is to be noted that the size of the neighborhood is a preset value, and the size of the neighborhood can be set as required, specifically, if a reference block with the size of 3 × 3 is determined with the reference pixel as a central point, a region where a pixel point except the reference pixel is located in the reference block is determined as the neighborhood of the reference pixel, further, a matching point Ineighbor (x ', y') of the reference pixel is respectively searched in two adjacent frames before and after the current frame, where the matching point satisfies the minimum value of var (M2/M1), where M1 is the neighborhood of the reference pixel, M2 is the neighborhood of the matching point, and var represents mean square error calculation;
carrying out weighted average calculation on the matching point of the previous adjacent frame and the reference pixel of the current frame, and carrying out weighted average calculation on the calculated gray value and the matching point of the next adjacent frame again to obtain the gray value of the target pixel;
and performing weighted average calculation on the matching points according to the following formula to obtain the gray value of the target pixel:
I(x,y)＝w*Ineighbor(x’,y’)+(1-w)*Icur(x,y)
wherein w is a weight value, w is exp (-var (M2/M1)/sigma ^2), sigma is a preset constant, which can be specifically set and adjusted according to the actual effect, w is less than or equal to 0.5, I (x, y) is the gray value of the target pixel, Icur (x, y) is the gray value of the reference pixel or the gray value obtained by calculation, the first weighted average calculation is to calculate the gray value of the reference pixel as Icur (x, y), the second weighted average calculation is to calculate the gray value obtained by calculation as Icur (x, y), ineghbor (x ', y') is the gray value of two matching point pixels, and (x, y) and (x ', y') in the above gray values are the coordinates of the corresponding pixel points;
and taking the image formed by the target pixels as the second result image, and outputting the second result image.
In this example, an image to be processed is obtained in real time, a motion vector of the image to be processed is determined through a preset search algorithm, if the motion vector is not a 0 vector, noise of a target type in the image to be processed is processed into gaussian distributed noise according to a preset transformation algorithm to obtain a noise processed image, the noise processed image is filtered according to a preset filtering algorithm, the noise of the target type in the filtered image is inversely transformed according to an inverse transformation algorithm of the preset transformation algorithm to obtain and output a first result image; if the motion vector is 0 vector, performing matching point weighted average calculation on the frame images of two adjacent frames before and after the current frame to obtain and output a second result image, searching the motion vector of the image first, and performing noise reduction processing in different modes respectively according to whether the motion vector is 0 vector, so that the noise reduction effect of the image can be improved, the processing speed is high, the real-time noise reduction of the image can be realized, and the information of the adjacent frames is fully utilized when the image is processed, so that the definition of the image can be further improved.
Referring to fig. 4, an embodiment of the present invention further provides an image processing apparatus, which may be a terminal or a module in the terminal, and may implement the image processing method, where the apparatus includes:
the motion estimation module 301 is configured to obtain an image to be processed in real time, and determine a motion vector of the image to be processed through a preset search algorithm;
a noise transformation module 302, configured to, if the motion vector is not a 0 vector, process the target type noise in the image to be processed into gaussian noise according to a preset transformation algorithm, so as to obtain a noise-processed image;
a filtering module 303, configured to filter the noise-processed image according to a preset filtering algorithm;
the noise transformation module 302 is further configured to perform inverse transformation on the noise of the target type in the filtered image according to an inverse transformation algorithm of the preset transformation algorithm to obtain a first result image;
an output module 304, configured to output the first result image;
the calculating module 305 is configured to perform weighted average calculation on matching points of frame images of two adjacent frames before and after the current frame if the motion vector is a 0 vector, so as to obtain a second result image.
The output module 304 is further configured to output the second result image.
Further, the motion estimation module 301 is further configured to determine two adjacent frames before and after the current frame of the image to be processed, and determine the motion vector in the two adjacent frames before and after through a three-step search algorithm.
The noise transformation module 302 is specifically configured to transform the additive noise in the image to be processed into gaussian distribution noise according to an anscombe transformation algorithm, so as to obtain the noise processed image.
The filtering module 303 is further configured to perform filtering preprocessing on the noise-processed image to obtain a preliminary filtering image of the noise-processed image;
confirming a reference block in the noise processing image, and confirming similar blocks of a plurality of reference blocks from the noise processing image according to a preset matching rule;
integrating a plurality of similar blocks in the noise processing image into a three-dimensional matrix, and carrying out coefficient scaling on the three-dimensional matrix through wiener filtering to realize filtering, wherein a scaling formula is as follows:
N(P)＝Twein_inverse(wp·Twein(Q1(P)))；
wherein N (P) represents a coefficient matrix; twein _ inverse () represents the three-dimensional inverse transform, wp being the wiener filter coefficient; twein (q (p)) represents a three-dimensional transformation of the three-dimensional matrix;
and transforming the three-dimensional matrix back to image estimation through three-dimensional inverse transformation, and restoring a plurality of similar blocks in the noise processing image to the original position of the noise processing image in a mode of weighting the value of the similar block at each corresponding position and a coefficient matrix N (P) to obtain the gray value of each pixel.
The noise transformation module 302 is further specifically configured to convert the additive noise in the filtered image from the gaussian distribution to the poisson distribution according to an inverse transformation algorithm of the ansscomb transformation algorithm.
A calculating module 305, further configured to select a reference pixel in the current frame and determine a neighborhood of the reference pixel;
searching a matching point of the reference pixel in the two adjacent frames, wherein the matching point meets the minimum value of var (M2/M1), wherein M1 is the neighborhood of the reference pixel, M2 is the neighborhood of the matching point, and var represents mean square error calculation;
carrying out weighted average calculation on the matching point of the previous adjacent frame and the reference pixel of the current frame, and carrying out weighted average calculation on the calculated gray value and the matching point of the next adjacent frame again to obtain the gray value of the target pixel;
and performing weighted average calculation on the matching points according to the following formula to obtain the gray value of the target pixel:
I(x,y)＝w*Ineighbor(x’,y’)+(1-w)*Icur(x,y)
wherein w is a weight value, w is exp (-var (M2/M1)/sigma ^2), sigma is a preset constant, which can be specifically set and adjusted according to the actual effect, w is less than or equal to 0.5, I (x, y) is the gray value of the target pixel, Icur (x, y) is the gray value of the reference pixel, or the gray value obtained by the calculation, the first weighted average calculation is to calculate the gray value of the reference pixel as Icur (x, y), the second weighted average calculation is to calculate the gray value obtained by the calculation as Icur (x, y), and the Ineighbor (x ', y') is the gray value of the two matching point pixels;
and taking the image formed by the target pixels as the second result image, and outputting the second result image.
In the embodiment, an image to be processed is obtained in real time, a motion vector of the image to be processed is determined through a preset search algorithm, if the motion vector is not a 0 vector, noise of a target type in the image to be processed is processed into Gaussian distribution noise according to a preset conversion algorithm to obtain a noise processing image, the noise processing image is filtered according to a preset filtering algorithm, the noise of the target type in the filtered image is inversely converted according to an inverse conversion algorithm of the preset conversion algorithm to obtain and output a first result image; if the motion vector is 0 vector, performing matching point weighted average calculation on the frame images of two adjacent frames before and after the current frame to obtain and output a second result image, searching the motion vector of the image first, and performing noise reduction processing in different modes respectively according to whether the motion vector is 0 vector, so that the noise reduction effect of the image can be improved, the processing speed is high, the real-time noise reduction of the image can be realized, and the information of the adjacent frames is fully utilized when the image is processed, so that the definition of the image can be further improved.
Referring to fig. 5, an embodiment of the present invention further provides a terminal 4, which includes a memory 401, a processor 402, and a computer program stored in the memory 401 and executable on the processor 402, and when the processor 402 executes the computer program, the steps of the image processing method in the embodiments shown in fig. 1 to 3 are implemented.
The Processor 402 may be a Central Processing Unit (CPU), other general purpose Processor such as a microprocessor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc.
The memory 401 may include read-only memory and random access memory, and may also include non-volatile random access memory.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when being executed by a processor, the computer program implements the steps of the image processing method in the embodiments shown in fig. 1 to 3.
The computer program includes computer program code, and the computer program code may be in a source code form, an object code form, an executable file or some intermediate form.
The computer-readable storage medium may include: any entity or device capable of carrying the above-described computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer readable Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signal, telecommunication signal, software distribution medium, etc.
In view of the above description of the image processing method, the image processing apparatus, the terminal and the computer readable storage medium provided by the present invention, those skilled in the art will recognize that changes may be made in the embodiments and applications of the invention in light of the above description, and therefore the disclosure of the present invention should not be interpreted as limiting the scope of the invention.
Claims (10)
1. An image processing method, comprising:
acquiring an image to be processed in real time, and determining a motion vector of the image to be processed through a preset search algorithm;
if the motion vector is not a 0 vector, processing the target type noise in the image to be processed into Gaussian distribution noise according to a preset transformation algorithm to obtain a noise processing image;
filtering the noise processing image according to a preset filtering algorithm, and performing inverse transformation on the noise of the target type in the filtered image according to an inverse transformation algorithm of the preset transformation algorithm to obtain and output a first result image;
and if the motion vector is a 0 vector, performing weighted average calculation on matching points of frame images of two adjacent frames before and after the current frame to obtain and output a second result image.
2. The method according to claim 1, wherein the determining the motion vector of the image to be processed by the preset search algorithm comprises:
determining two adjacent frames before and after the current frame of the image to be processed;
and determining the motion vector in the front and the back two adjacent frames by a three-step search algorithm.
3. The method according to claim 1 or 2, wherein the processing the target type noise in the image to be processed into gaussian distributed noise according to a preset transformation algorithm, and obtaining a noise-processed image comprises:
and according to an anscombe transformation algorithm, transforming the additive noise in the image to be processed into Gaussian distribution noise to obtain the noise processing image.
4. The method of claim 3, wherein filtering the noise-processed image according to a predetermined filtering algorithm comprises:
confirming a reference block in the noise processing image, and confirming similar blocks of a plurality of reference blocks from the noise processing image according to a preset matching rule;
integrating a plurality of similar blocks in the noise-processed image into a three-dimensional matrix;
and carrying out coefficient scaling on the three-dimensional matrix through wiener filtering to realize filtering, wherein a scaling formula is as follows:
N(P)＝Twein_inverse(wp·Twein(Q1(P)))；
wherein N (P) represents a coefficient matrix; twein _ inverse () represents the three-dimensional inverse transform, wp being the wiener filter coefficient; twein (q (p)) represents a three-dimensional transformation of the three-dimensional matrix;
and transforming the three-dimensional matrix back to image estimation through three-dimensional inverse transformation, and restoring a plurality of similar blocks in the noise processing image to the original position of the noise processing image in a mode of weighting the value of the similar block at each corresponding position and the coefficient matrix to obtain the gray value of each pixel.
5. The method according to claim 4, wherein said inverse transforming the noise of the target type in the filtered image according to the inverse transformation algorithm of the preset transformation algorithm comprises:
and converting the additive noise in the filtered image from the Gaussian distribution to the Poisson distribution according to the inverse transformation algorithm of the anscombe transformation algorithm.
6. The method of claim 5, wherein the performing the weighted average calculation of the matching points on the frame images of two adjacent frames before and after the current frame to obtain and output the second result image comprises:
selecting a reference pixel in the current frame, and determining a neighborhood of the reference pixel;
searching matching points of the reference pixel in the two adjacent frames respectively, wherein each matching point meets the minimum value of var (M2/M1), wherein M1 is the neighborhood of the reference pixel, M2 is the neighborhood of the matching point, and var represents mean square error calculation;
carrying out weighted average calculation on the matching points of the previous adjacent frame and the reference pixels of the current frame, and carrying out weighted average calculation on the calculated gray value and the matching points of the next adjacent frame again to obtain the gray value of the target pixel:
the formula of the above two average weighting calculations is:
I(x,y)＝w*Ineighbor(x’,y’)+(1-w)*Icur(x,y)
wherein w is a weight value, w is exp (-var (M2/M1)/sigma ^2), sigma is a constant, w is less than or equal to 0.5, I (x, y) is the gray value of the target pixel, Icur (x, y) is the gray value of the reference pixel or the gray value obtained by calculation, Ineighbor (x ', y') is the gray values of the two matching point pixels, and (x, y) and (x ', y') are the coordinates of the corresponding pixel points respectively;
and taking the image formed by the target pixels as the second result image, and outputting the second result image.
7. An image processing apparatus characterized by comprising:
the motion estimation module is used for acquiring an image to be processed in real time and determining a motion vector of the image to be processed through a preset search algorithm;
the noise transformation module is used for processing the target type noise in the image to be processed into Gaussian distribution noise according to a preset transformation algorithm if the motion vector is not a 0 vector, so as to obtain a noise processing image;
the filtering module is used for filtering the noise processing image according to a preset filtering algorithm;
the noise transformation module is further configured to perform inverse transformation on the noise of the target type in the filtered image according to an inverse transformation algorithm of the preset transformation algorithm to obtain a first result image;
an output module for outputting the first result image;
the calculation module is used for performing matching point weighted average calculation on the frame images of two adjacent frames before and after the current frame to obtain a second result image if the motion vector is a 0 vector;
the output module is further configured to output the second result image.
8. The apparatus of claim 7, wherein the filtering module is further configured to identify a reference block in the noise-processed image, and identify similar blocks of a plurality of reference blocks from the noise-processed image according to a preset matching rule;
integrating a plurality of similar blocks in the noise-processed image into a three-dimensional matrix;
and carrying out coefficient scaling on the three-dimensional matrix through wiener filtering to realize filtering, wherein a scaling formula is as follows:
N(P)＝Twein_inverse(wp·Twein(Q1(P)))；
wherein N (P) represents a coefficient matrix; twein _ inverse () represents the three-dimensional inverse transform, wp being the wiener filter coefficient; twein (q (p)) represents a three-dimensional transformation of the three-dimensional matrix;
and transforming the three-dimensional matrix back to image estimation through three-dimensional inverse transformation, and restoring a plurality of similar blocks in the noise processing image to the original position of the noise processing image in a mode of weighting the value of the similar block at each corresponding position and the coefficient matrix to obtain the gray value of each pixel.
9. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the image processing method according to any of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the image processing method according to any one of claims 1 to 6.
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