CN102479380B - Image interpolation method capable of improving image resolution and device utilizing image interpolation method - Google Patents

Image interpolation method capable of improving image resolution and device utilizing image interpolation method Download PDF

Info

Publication number
CN102479380B
CN102479380B CN201010558745.1A CN201010558745A CN102479380B CN 102479380 B CN102479380 B CN 102479380B CN 201010558745 A CN201010558745 A CN 201010558745A CN 102479380 B CN102479380 B CN 102479380B
Authority
CN
China
Prior art keywords
resolution
vegetarian refreshments
pixel
selected window
partial structurtes
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Fee Related
Application number
CN201010558745.1A
Other languages
Chinese (zh)
Other versions
CN102479380A (en
Inventor
任杰
刘家瑛
郭宗明
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
New Founder Holdings Development Co ltd
Peking University
Beijing Founder Electronics Co Ltd
Original Assignee
Peking University
Peking University Founder Group Co Ltd
Beijing Founder Electronics Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Peking University, Peking University Founder Group Co Ltd, Beijing Founder Electronics Co Ltd filed Critical Peking University
Priority to CN201010558745.1A priority Critical patent/CN102479380B/en
Publication of CN102479380A publication Critical patent/CN102479380A/en
Application granted granted Critical
Publication of CN102479380B publication Critical patent/CN102479380B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Landscapes

  • Image Processing (AREA)

Abstract

The invention discloses an image interpolation method capable of improving image resolution and a device utilizing the image interpolation method, aiming to overcome defects at boundaries of interpolated images in the prior art. The image interpolation method capable of improving image resolution includes steps of determining similarity probability weight between all pixels in a local window and the reference pixel, modulating contribution of each pixel to the integral error in optimization estimation according to the similarity probability weight, and finally calculating all the pixel values to be estimated in the local window by estimating according to the weighted least square process. Since the similarity probability weight is introduced to serve as the weight for estimation of high-resolution pixels, estimation error is minimized, and defects at the boundaries are reduced.

Description

A kind of image interpolation method and device that improves image resolution ratio
Technical field
The invention belongs to technical field of image processing, particularly a kind of image interpolation method and device that improves image resolution ratio.
Background technology
Image is one of mankind's main carriers of obtaining outside objective world information.Along with human society enters the information age, computer technology, modern communications technology and the information processing technology have obtained development fast, and consumption electronic product has spread all over each corner of human lives.Along with the appearance of digital picture and application widely, people are also more and more higher to the requirement of picture quality, in the situation that raising image capture device hardware capabilities has approached the limit, adopting software engineering to improve original image resolution ratio becomes one of hot issue of digital image processing field gradually.
Image interpolation is traditional one of method improving image resolution ratio that is used for.The interpolation method of traditional digital picture has a variety of, wherein fastest is Nearest Neighbor (nearest-neighbor) method, the method by copy be inserted into a little adjacent nearest pixel value as the pixel value of insertion point, this method speed is fast, realize simply, but in interpolation image, often there will be zigzag flaw; Bilinear (bilinearity) interpolation method is by getting each mean value that is inserted into four immediate pixel numerical value a little as insertion point pixel value, this method has been eliminated zigzag phenomenon, but computing time is slightly long, and easily there is blurring effect in the boundary in image.Bicubic (bicubic) convolution method by interpolation point (i+u, j+v) around 16 neighbor pixel points take into account, by following interpolation formula, can obtain the pixel value f (i+u, j+v) of this interpolation point:
f(i+u，j+v)＝A*B*C
A＝[s(u+1)s(u+0)s(u-1)s(u-2)]
B = f ( i - 1 , j - 1 ) f ( i - 1 , j + 0 ) f ( i - 1 , j + 1 ) f ( i - 1 , j + 2 ) f ( i + 0 , j - 1 ) f ( i + 0 , j + 0 ) f ( i + 0 , j + 1 ) f ( i + 0 , j + 2 ) f ( i + 1 , j - 1 ) f ( i + 1 , j + 0 ) f ( i + 1 , j + 1 ) f ( i + 1 , j + 2 ) f ( i + 2 , j - 1 ) f ( i + 2 , j + 0 ) f ( i + 2 , j + 1 ) f ( i + 2 , j + 2 )
C＝[s(v+1)s(v+0)s(v-1)s(v-2)] T
s ( x ) = 1 - 2 | x | 2 + | x | 3 0 &le; | x | < 1 4 - 8 | x | + 5 | x | 2 - | x | 3 1 &le; | x | < 2 0 | x | &GreaterEqual; 2
Compare arest neighbors and bilinear interpolation, bicubic convolution method has greatly improved to image interpolation effect tool, and interpolation place numerical value and derivative are all continuous, and details performance is clearer, has overcome to a certain extent the image flaw of above two kinds of methods.But owing to relating to a cube computing in calculating formula, and have matrix convolution, so calculated amount is compared, and the above two are larger.
Comparatively level and smooth region in image, above classic method as Bilinear and Bicubic can process fine.But for the image-region at boundary, above traditional interpolation method has obvious flaw, as fuzzy, sawtooth etc.And human visual system is very responsive for boundary characteristic, therefore make this flaw cause larger harmful effect to image visual effect.Therefore, a main target that realizes good image interpolation is how to keep better image in the feature of boundary when interpolation, avoids producing the various flaws of traditional interpolation method.In order to realize such target, need to carry out comparatively reasonably modeling to picture signal.The difficult point of picture signal modeling is the unstable state characteristic of himself.Meanwhile, natural image often has geometrical rule, and image changes slowly on boundary direction, in the direction perpendicular to border, changes rapidly.This character has reflected the segmentation statistic steady state characteristic of picture signal.According to such a characteristic, the people such as Li have proposed a kind of adaptive interpolation method that utilizes covariance information to carry out Latent Including boundary information.High resolving power covariance information estimates by low resolution covariance, low resolution covariance information is that in hypothesis window, statistic keeps stable, samples and estimate in a local window.This method can be processed the border condition compared with large scale in image well, but cannot process well some compared with the border of small scale and textural characteristics, simultaneously because its method has only been utilized the neighbor pixel dot information in diagonal, the interpolation sharpness on border is compared original image and is also had a certain distance, and the narrow and small edge effect image visual effect that has some falsenesses in interpolation image, seems to lack nature and the sense of reality.Zhang and Wu introduce into more pixel space correlationship, not only utilized the correlationship of diagonal, and considered the correlationship in horizontal and vertical direction, by as if statistics amount in a local window, keep stable, this correlationship restriction is forced on pixels all in window, by piece optimization, estimate finally to obtain the value of one group of pixel to be inserted.This method takes full advantage of the correlationship between high low-resolution image, for the structural information of natural image, has good adaptability, therefore larger improvement the people's such as Li algorithm.
Above two kinds of algorithms are all that hypothesis image statistics in selected window keeps stable, when image boundary characteristic dimension is less than window size, this hypothesis is also false, therefore the estimation for statistic information in window is inaccurate, cause the error of system model, thereby cause interpolation image to occur flaw at boundary.
Summary of the invention
In order to solve prior art interpolation image, occur the problem of flaw at boundary, the embodiment of the present invention provides a kind of image interpolation method that improves image resolution ratio, comprising:
Similarity probability between the partial structurtes of default reference image vegetarian refreshments in the partial structurtes of each pixel and selected window in the selected window of computed image;
According to the pixel value of known low-resolution pixel point in selected window, the first parameter and the second parameter are estimated, the first parameter is for portraying in window the model parameter of neighbor pixel point regression relation on each pixel and its diagonal, the second parameter be for portray interior each pixel of window perpendicular with horizontal line direction on the model parameter of neighbor pixel point regression relation;
Utilize the estimated result of the pixel value of low-resolution pixel point, the estimated result of the first parameter and the second parameter, set up the pixel value of interpolation full-resolution picture vegetarian refreshments in selected window and the correlationship of matching error;
According to similarity probability and matching error, set up optimization aim function, optimization aim function is carried out to the pixel value that optimum estimate obtains full-resolution picture vegetarian refreshments;
The pixel value of the full-resolution picture vegetarian refreshments obtaining by optimum estimate, the full-resolution picture vegetarian refreshments that similarity probability is greater than to setting threshold inserts selected window.
The embodiment of the present invention also provides a kind of image interpolation device that improves image resolution ratio simultaneously, comprising:
Probability generation module, for the similarity probability between the partial structurtes of the partial structurtes of each pixel in the selected window of computed image and the default reference image vegetarian refreshments of selected window;
The first estimation module, for the first parameter and the second parameter being estimated according to the pixel value of known low-resolution pixel point in selected window, the first parameter is for portraying in window the model parameter of neighbor pixel point regression relation on each pixel and its diagonal, the second parameter be for portray interior each pixel of window perpendicular with horizontal line direction on the model parameter of neighbor pixel point regression relation;
Correlationship is set up module, for utilizing the estimated result of the pixel value of low-resolution pixel point, the estimated result of the first parameter and the second parameter, sets up the pixel value of interpolation full-resolution picture vegetarian refreshments in selected window and the correlationship of matching error;
The second estimation module, for setting up optimization aim function according to similarity probability and matching error, carries out to optimization aim function the pixel value that optimum estimate obtains full-resolution picture vegetarian refreshments;
Insert module, for the pixel value of the full-resolution picture vegetarian refreshments that obtains by optimum estimate, the full-resolution picture vegetarian refreshments that similarity probability is greater than to setting threshold inserts selected window.
By specific embodiments provided by the invention, can be found out, the weight when having introduced similarity probability as high-resolution pixel point estimation, and then evaluated error is minimized, reduced the flaw that boundary occurs.
Accompanying drawing explanation
Fig. 1 is twice interpolation schematic diagram provided by the invention;
Fig. 2 is the first embodiment method flow diagram provided by the invention;
Fig. 3 is the local window schematic diagram with different similarity probability provided by the invention;
Fig. 4 is two right schematic diagram of pixel with different similarity probability provided by the invention;
Fig. 5 a is that the low resolution neighbours in 4 diagonals utilizing each full-resolution picture vegetarian refreshments provided by the invention put pixel value and form partial structurtes vector schematic diagram;
Fig. 5 b is that the low resolution neighbours in 4 diagonals utilizing each low-resolution pixel point provided by the invention put pixel value and form partial structurtes vector schematic diagram;
Fig. 6 a has consistent model parameter schematic diagram in horizontal and vertical direction between high low resolution pixel provided by the invention;
Fig. 6 b has consistent model parameter schematic diagram in diagonal between high low resolution pixel provided by the invention;
Fig. 7 is the configuration schematic diagram of the space correlation relation between interpolation neighbours' point provided by the invention;
Fig. 8 is the second embodiment system construction drawing provided by the invention.
Embodiment
In order to solve interpolation image in prior art, at boundary, there is the problem of flaw, the embodiment of the present invention provides a kind of image interpolation method that improves image resolution ratio, in the scheme of the present embodiment, without loss of generality, regarding in high-definition picture the pixel in low-resolution image as down-sampling that the factor through rule is 2 obtains, therefore these pixels can be regarded the part in original high resolution image as, and Fig. 1 has provided interpolation schematic diagram twice.From existing low-resolution pixel, select 10 interpolation goes out first's full-resolution picture vegetarian refreshments 11 and second portion full-resolution picture vegetarian refreshments 12 to the task of interpolation exactly so.Whole Interpolation Process is divided into two steps.The first step is that interpolation obtains first's full-resolution picture vegetarian refreshments 11.Second step is that first's full-resolution picture vegetarian refreshments 11 of obtaining according to first step interpolation and existing low-resolution pixel are selected 10 and come interpolation to obtain second portion full-resolution picture vegetarian refreshments 12.The algorithm that two steps are processed is the same, only in direction, has the rotation of 45 degree and the scaling on yardstick.Therefore, the core objective of interpolation algorithm can be regarded as from existing low-resolution pixel and selects the process that 10 interpolation go out first's full-resolution picture vegetarian refreshments 11.
The image interpolation method of raising image resolution ratio provided by the invention, refers to Fig. 2, comprises the steps:
Step 101, the similarity probability P between the partial structurtes of center pixel to be inserted in the partial structurtes of each pixel and selected window in the selected window of computed image land P h.
For convenience of explanation, the present embodiment is using center pixel to be inserted default reference image vegetarian refreshments in selected window, but this is not limited, reference image vegetarian refreshments can be also any one pixel that comprises first pixel and last pixel in the selected window of image.In local window centered by the pixel to be inserted of the present embodiment Shi Yi center, the pixel value of high-resolution pixel in this window is estimated.The local window with different similarity probability arranging is like this shown by Fig. 3, comprises first 21, second portion 22 and third part 23.In such a local window, not all pixel all has the similar topography's feature of concentricity pixel to be inserted 20, be partial structurtes dissimilar, there are some may also there is larger difference, the pixel of first 21 and local window center pixel local structure similarity to be inserted wherein, the pixel of second portion 22 and third part 23 and local window center pixel 20 partial structurtes to be inserted are dissimilar, therefore need to have a kind of index to react the similarity difference of partial structurtes between each pixel.This similarity difference can be measured by the similarity probability between them.
In the partial structurtes of j pixel and selected window, the computing formula as the similarity probability between the partial structurtes of d pixel of reference image vegetarian refreshments is in window
Figure BSA00000359809600061
Similarity probability between the partial structurtes that are the present embodiment Zhong center pixel to be inserted as d pixel of reference image vegetarian refreshments in the partial structurtes that wherein p (d, j) is j pixel in selected window and selected window,
Figure BSA00000359809600062
Figure BSA00000359809600063
be the vector of the pixel value formation of order of the neighbours in four diagonals of center pixel to be inserted in selected window, d is positive integer, d≤m+n, n is the number of selected window middle high-resolution pixel, m is the number of low-resolution pixel point, and ε is one and avoids the positive number except zero overflow error
Figure BSA00000359809600064
Figure BSA00000359809600065
it is the vector of the pixel value formation of ordering of the neighbours in four diagonals of j pixel in selected window, j is positive integer, j≤m+n, h is used for the distribution of shapes parameter of control characteristic function, and Fig. 4 is two right schematic diagram of pixel with different similarity probability.
According to above formula
Figure BSA00000359809600066
can by the similarity probability calculation between the concentricity pixel to be inserted of all pixels in window out, form two probability vector P land P h, wherein P L = ( p 1 L , . . . , p i L , . . . , p m L ) T , P H = ( p 1 H , . . . , p k H , . . . , p n H ) T .
Specific as follows, utilize the low resolution neighbours' point in four diagonals of low-resolution pixel point, similarity probability in the partial structurtes that in the selected window of calculating, each known low-resolution pixel is selected and selected window between the partial structurtes of default reference image vegetarian refreshments, and form low-resolution pixel point similarity probability vector P l, wherein
Figure BSA00000359809600071
represent the similarity probability between the partial structurtes of reference image vegetarian refreshments default in the 1st partial structurtes that low-resolution pixel is selected and selected window,
Figure BSA00000359809600073
represent the similarity probability i≤m between the partial structurtes of reference image vegetarian refreshments default in partial structurtes that i low-resolution pixel selected and selected window,
Figure BSA00000359809600074
represent the similarity probability between the partial structurtes of reference image vegetarian refreshments default in partial structurtes that m low-resolution pixel selected and selected window.Utilize the low resolution neighbours' point in four diagonals of full-resolution picture vegetarian refreshments, similarity probability between the partial structurtes of default reference image vegetarian refreshments in the partial structurtes of each full-resolution picture vegetarian refreshments and selected window in the selected window of calculating, and form full-resolution picture vegetarian refreshments probability vector P h, wherein
Figure BSA00000359809600075
Figure BSA00000359809600076
represent the similarity probability between the partial structurtes of reference image vegetarian refreshments default in the partial structurtes of the 1st full-resolution picture vegetarian refreshments and selected window, represent the similarity probability k≤n between the partial structurtes of reference image vegetarian refreshments default in the partial structurtes of k full-resolution picture vegetarian refreshments and selected window,
Figure BSA00000359809600078
represent the similarity probability between the partial structurtes of reference image vegetarian refreshments default in the partial structurtes of n full-resolution picture vegetarian refreshments and selected window.When the similarity probability calculating between full-resolution picture vegetarian refreshments and center pixel to be inserted, can directly utilize low resolution neighbours in 4 diagonals of each full-resolution picture vegetarian refreshments to put pixel value and form partial structurtes vector utilize low resolution neighbours in 4 diagonals of each full-resolution picture vegetarian refreshments to put pixel value and form partial structurtes vector as shown in Figure 5 a, then directly utilize formula
Figure BSA000003598096000710
calculate.When the similarity probability calculating between the pixel to be inserted of low-resolution pixel Dian He center, due to low-resolution pixel, selecting that neighbours in diagonal select is unknown full-resolution picture vegetarian refreshments, can be according to the structural similarity hypothesis between high low resolution, utilize low resolution neighbours in its diagonal to put pixel value and form
Figure BSA000003598096000711
utilize low resolution neighbours in 4 diagonals of each low-resolution pixel point to put pixel value and form partial structurtes vector as shown in Figure 5 b.
Step 102, according to the pixel value of known low-resolution pixel point in selected window, parameter a and parameter b are estimated, parameter a is for portraying in window the model parameter of neighbor pixel point regression relation on each pixel and its diagonal, parameter b be for portray interior each pixel of window perpendicular with horizontal line direction on the model parameter of neighbor pixel point regression relation.
In the present embodiment, utilize known low-resolution pixel to select to estimate unknown full-resolution picture vegetarian refreshments, need to carry out modeling to the relation between them.Adopt two group model parameter a, b to portray respectively this space correlation relation.With
Figure BSA00000359809600081
represent the regression relation in diagonal, use
Figure BSA00000359809600082
represent the regression relation in horizontal and vertical direction, with t ∈, { 1,2,3,4} represents t neighbours in respective direction.For example
Figure BSA00000359809600083
with
Figure BSA00000359809600084
represent respectively pixel x iand y it neighbours in diagonal; with
Figure BSA00000359809600086
represent respectively pixel x iand y it neighbours' point in the horizontal and vertical directions.By supposing that the parameter between high low-resolution image is consistent, can to parameter a and b, estimate according to the pixel value of low-resolution image pixel in local window.Between high low resolution pixel, in horizontal and vertical direction, have consistent model parameter as shown in Figure 6 a, parameter b can directly be weighted least-squares estimation and try to achieve its optimal value.Between high low resolution pixel, in diagonal, there is consistent model parameter as shown in Figure 6 b, according to the conforming hypothesis of model parameter between high low resolution, yardstick is put and is twice, adopt
Figure BSA00000359809600087
to parameter, a estimates.
When parameter a, parameter b are estimated, directly pass through
Figure BSA00000359809600088
to parameter, a is weighted least-squares estimation, wherein
Figure BSA00000359809600089
represent the first parameter optimal value estimated result, a trepresent a vectorial composition value with the parameter a of vector representation, t ∈ { 1,2,3,4}, f (x i) represent i low-resolution pixel point x ipixel value,
Figure BSA000003598096000810
represent pixel x it low resolution neighbours point in diagonal pixel value.Directly pass through the second parameter is weighted to least square method and estimates, wherein
Figure BSA000003598096000813
represent the second parameter optimal value estimated result, b trepresent a vectorial composition value by the second parameter of vector representation,
Figure BSA000003598096000814
represent low-resolution pixel x it neighbours' point in vertical and horizontal line direction
Figure BSA000003598096000815
pixel value.
Step 103, utilizes the estimated result of the pixel value of low-resolution pixel point, the estimated result of parameter a and parameter b, sets up the pixel value of interpolation full-resolution picture vegetarian refreshments in selected window and the correlationship of matching error.
Pass through parameter
Figure BSA000003598096000816
carry out the correlationship in the diagonal of picture engraving part, can obtain
Figure BSA00000359809600091
Pass through parameter
Figure BSA00000359809600092
portray the correlationship in the horizontal and vertical direction of topography, can obtain
Figure BSA00000359809600093
and
Figure BSA00000359809600094
In integrating step 102, describe, adopt two group model parameter a, b to portray respectively this space correlation relation.With represent the regression relation in diagonal, use represent the regression relation in horizontal and vertical direction, with t ∈, { 1,2,3,4} represents t neighbours in respective direction, and between interpolation neighbours' point, the configuration schematic diagram of space correlation relation as shown in Figure 7.
The correlationship of carrying out picture engraving local space by use a model parameter a and b, the matching error of above-mentioned expression can be expressed as following form:
Figure BSA00000359809600097
Figure BSA00000359809600098
expression is passed through
Figure BSA00000359809600099
portray full-resolution picture vegetarian refreshments y k 'with neighbor pixel point in its diagonal
Figure BSA000003598096000910
matching error during regression relation, Q represents to have neighbor pixel point in selected window inner opposite angle line direction
Figure BSA000003598096000911
full-resolution picture vegetarian refreshments y k 'number,
Figure BSA000003598096000912
expression is passed through
Figure BSA000003598096000914
portray low-resolution pixel point x i 'with neighbor pixel point in its diagonal matching error during regression relation, P represents to have neighbor pixel point in selected window inner opposite angle line direction
Figure BSA000003598096000916
low-resolution pixel point x i 'number, expression is passed through
Figure BSA000003598096000919
portray full-resolution picture vegetarian refreshments y k "with neighbor pixel point in its diagonal
Figure BSA000003598096000920
matching error during regression relation, K represents to have neighbor pixel point in vertical in selected window and horizontal line direction
Figure BSA000003598096000921
full-resolution picture vegetarian refreshments y k "number, expression is passed through
Figure BSA000003598096000924
portray low-resolution pixel point x i "with neighbor pixel point in its diagonal
Figure BSA000003598096000925
matching error during regression relation, S represents to have neighbor pixel point in vertical in selected window and horizontal line direction
Figure BSA000003598096000926
low-resolution pixel point x i "number.
Step 104, according to similarity probability P land P hset up optimization aim function F with matching error, optimization aim function F is carried out to the best estimate that optimum estimate obtains the full-resolution picture vegetarian refreshments in a group window.
Set up optimization aim function
Figure BSA00000359809600101
meet following constraint condition simultaneously
Figure BSA00000359809600102
wherein F is optimization aim function, and f (y) represents the pixel value of full-resolution picture vegetarian refreshments, and w represents selected window.By solving this Estimation Optimization problem, finally can obtain the best estimate of the full-resolution picture vegetarian refreshments in a group window.
The best estimate of full-resolution picture vegetarian refreshments carries out to optimization aim function the pixel value that optimum estimate obtains full-resolution picture vegetarian refreshments.
Step 105, the pixel value of the full-resolution picture vegetarian refreshments obtaining by optimum estimate, the best estimate of output center pixel to be inserted.
Wherein, center pixel to be inserted is the main interpolation point of paying close attention in the present embodiment, therefore only exports the numerical value of this point.In order to reduce the time complexity of total algorithm, can consider the value of the higher point of those and center pixel similarity to be inserted probability also to export as its final pixel value, be about to the full-resolution picture vegetarian refreshments that similarity probability is greater than setting threshold and insert selected window.
By local window being carried out on image to the slip in horizontal direction and vertical direction, we can, in the hope of the optimal estimation value of all pixels to be inserted, finally complete the process of image interpolation.
The second embodiment provided by the invention is a kind of image interpolation device that improves image resolution ratio, and its structure as shown in Figure 8, comprising:
Probability generation module 201, for the similarity probability between the partial structurtes of the partial structurtes of each pixel in the selected window of computed image and the default reference image vegetarian refreshments of selected window;
The first estimation module 202, for the first parameter and the second parameter being estimated according to the pixel value of known low-resolution pixel point in selected window, the first parameter is for portraying in window the model parameter of neighbor pixel point regression relation on each pixel and its diagonal, the second parameter be for portray interior each pixel of window perpendicular with horizontal line direction on the model parameter of neighbor pixel point regression relation;
Correlationship is set up module 203, for utilizing the estimated result of the pixel value of low-resolution pixel point, the estimated result of the first parameter and the second parameter, sets up the pixel value of interpolation full-resolution picture vegetarian refreshments in selected window and the correlationship of matching error;
The second estimation module 204, for setting up optimization aim function according to similarity probability and matching error, carries out to optimization aim function the pixel value that optimum estimate obtains full-resolution picture vegetarian refreshments;
Insert module 205, for the pixel value of the full-resolution picture vegetarian refreshments that obtains by optimum estimate, the full-resolution picture vegetarian refreshments that similarity probability is greater than to setting threshold inserts selected window.
Further, probability generation module 201, also for passing through
Figure BSA00000359809600111
carry out similarity probability calculation, in the partial structurtes that wherein p (d, j) is j pixel in selected window and selected window as the similarity probability between the partial structurtes of d pixel of reference image vegetarian refreshments,
Figure BSA00000359809600113
be the vector of the pixel value formation of ordering of the neighbours in four diagonals of d pixel in selected window, d is positive integer, d≤m+n, n is the number of selected window middle high-resolution pixel, m is the number of low-resolution pixel point, and ε is one and avoids the positive number except zero overflow error
Figure BSA00000359809600114
be the vector of the pixel value formation of ordering of the neighbours in four diagonals of j pixel in selected window, j is positive integer, j≤m+n, and h is used for the distribution of shapes parameter of control characteristic function.
Further, probability generation module 201, also for utilizing the low resolution neighbours' point in four diagonals of low-resolution pixel point, similarity probability in the partial structurtes that in the selected window of calculating, each known low-resolution pixel is selected and selected window between the partial structurtes of default reference image vegetarian refreshments, and form low-resolution pixel point similarity probability vector P l, wherein
Figure BSA00000359809600115
Figure BSA00000359809600116
represent the similarity probability between the partial structurtes of reference image vegetarian refreshments default in the 1st partial structurtes that low-resolution pixel is selected and selected window,
Figure BSA00000359809600117
represent the similarity probability i≤m between the partial structurtes of reference image vegetarian refreshments default in partial structurtes that i low-resolution pixel selected and selected window,
Figure BSA00000359809600118
represent the similarity probability between the partial structurtes of reference image vegetarian refreshments default in partial structurtes that m low-resolution pixel selected and selected window; And
Utilize the low resolution neighbours' point in four diagonals of full-resolution picture vegetarian refreshments, similarity probability between the partial structurtes of default reference image vegetarian refreshments in the partial structurtes of each full-resolution picture vegetarian refreshments and selected window in the selected window of calculating, and form full-resolution picture vegetarian refreshments probability vector P h, wherein represent the similarity probability between the partial structurtes of reference image vegetarian refreshments default in the partial structurtes of the 1st full-resolution picture vegetarian refreshments and selected window,
Figure BSA00000359809600121
represent the similarity probability k≤n between the partial structurtes of reference image vegetarian refreshments default in the partial structurtes of k full-resolution picture vegetarian refreshments and selected window,
Figure BSA00000359809600122
represent the similarity probability between the partial structurtes of reference image vegetarian refreshments default in the partial structurtes of n full-resolution picture vegetarian refreshments and selected window.
Further, the first estimation module 202, also for directly passing through
Figure BSA00000359809600123
the first parameter is weighted to least-squares estimation, wherein
Figure BSA00000359809600124
represent the first parameter optimal value estimated result, a trepresent a vectorial composition value by the first parameter of vector representation, t ∈ { 1,2,3,4}, f (x i) represent i low-resolution pixel point x ipixel value,
Figure BSA00000359809600125
represent pixel x it low resolution neighbours point in diagonal
Figure BSA00000359809600126
pixel value;
Directly pass through the second parameter is weighted to least square method and estimates, wherein
Figure BSA00000359809600128
represent the second parameter optimal value estimated result, b trepresent a vectorial composition value by the second parameter of vector representation,
Figure BSA00000359809600129
represent low-resolution pixel x it neighbours' point in vertical and horizontal line direction
Figure BSA000003598096001210
pixel value.
Further, the second estimation module 204, also for setting up optimization aim function
Figure BSA000003598096001211
meet following constraint condition simultaneously
Figure BSA000003598096001212
wherein F is optimization aim function, and f (y) represents the pixel value of full-resolution picture vegetarian refreshments, and w represents selected window,
Figure BSA000003598096001213
Figure BSA000003598096001214
expression is passed through
Figure BSA000003598096001215
portray full-resolution picture vegetarian refreshments y k 'with neighbor pixel point in its diagonal
Figure BSA000003598096001216
matching error during regression relation, Q represents to have neighbor pixel point in selected window inner opposite angle line direction
Figure BSA000003598096001217
full-resolution picture vegetarian refreshments y k 'number, expression is passed through portray low-resolution pixel point x i 'with neighbor pixel point in its diagonal matching error during regression relation, P represents to have neighbor pixel point in selected window inner opposite angle line direction
Figure BSA00000359809600131
low-resolution pixel point x i 'number,
Figure BSA00000359809600132
Figure BSA00000359809600133
expression is passed through
Figure BSA00000359809600134
portray full-resolution picture vegetarian refreshments y k "with neighbor pixel point in its diagonal matching error during regression relation, K represents to have neighbor pixel point in vertical in selected window and horizontal line direction
Figure BSA00000359809600136
full-resolution picture vegetarian refreshments y k "number,
Figure BSA00000359809600137
expression is passed through
Figure BSA00000359809600139
portray low-resolution pixel point x i "with neighbor pixel point in its diagonal
Figure BSA000003598096001310
matching error during regression relation, S represents to have neighbor pixel point in vertical in selected window and horizontal line direction low-resolution pixel point x i "number.
Further, insert module 205, if be also full-resolution picture vegetarian refreshments for reference image vegetarian refreshments, inserts selected window using the full-resolution picture vegetarian refreshments as reference image vegetarian refreshments.
Obviously, those skilled in the art can carry out various changes and modification and not depart from the spirit and scope of the present invention the present invention.Like this, if within of the present invention these are revised and modification belongs to the scope of the claims in the present invention and equivalent technologies thereof, the present invention is also intended to comprise these changes and modification interior.

Claims (10)

1. an image interpolation method that improves image resolution ratio, is characterized in that, comprising:
Similarity probability between the partial structurtes of default reference image vegetarian refreshments in the partial structurtes of each pixel and selected window in the selected window of computed image;
According to the pixel value of known low-resolution pixel point in selected window, the first parameter and the second parameter are estimated, the first parameter is for portraying in window the model parameter of neighbor pixel point regression relation on each pixel and its diagonal, the second parameter be for portray interior each pixel of window perpendicular with horizontal line direction on the model parameter of neighbor pixel point regression relation;
Utilize the estimated result of the pixel value of low-resolution pixel point, the estimated result of the first parameter and the second parameter, set up the pixel value of interpolation full-resolution picture vegetarian refreshments in selected window and the correlationship of matching error;
According to similarity probability and matching error, set up optimization aim function, optimization aim function is carried out to the pixel value that optimum estimate obtains full-resolution picture vegetarian refreshments;
The pixel value of the full-resolution picture vegetarian refreshments obtaining by optimum estimate, the full-resolution picture vegetarian refreshments that similarity probability is greater than to setting threshold inserts selected window; Be specially: if reference image vegetarian refreshments is full-resolution picture vegetarian refreshments, the full-resolution picture vegetarian refreshments as reference image vegetarian refreshments is inserted to selected window.
2. the method for claim 1, is characterized in that, in the selected window of computed image, in the partial structurtes of each pixel and selected window, the similarity probability between the partial structurtes of default reference image vegetarian refreshments is specially:
By
Figure FDA0000376735910000011
carry out similarity probability calculation, in the partial structurtes that wherein p (d, j) is j pixel in selected window and selected window as the similarity probability between the partial structurtes of d pixel of reference image vegetarian refreshments,
Figure FDA0000376735910000012
Figure FDA0000376735910000013
be the vector of the pixel value formation of ordering of the neighbours in four diagonals of d pixel in selected window, d is positive integer, d≤m+n, n is the number of selected window middle high-resolution pixel, m is the number of low-resolution pixel point, and ε is one and avoids the positive number except zero overflow error
Figure FDA0000376735910000014
Figure FDA0000376735910000015
be the vector of the pixel value formation of ordering of the neighbours in four diagonals of j pixel in selected window, j is positive integer, j≤m+n, and h is used for the distribution of shapes parameter of control characteristic function.
3. method as claimed in claim 2, is characterized in that, carries out similarity probability calculation and is specially:
Utilize the low resolution neighbours' point in four diagonals of low-resolution pixel point, similarity probability in the partial structurtes that in the selected window of calculating, each known low-resolution pixel is selected and selected window between the partial structurtes of default reference image vegetarian refreshments, and form low-resolution pixel point similarity probability vector P l, wherein
Figure FDA0000376735910000021
Figure FDA0000376735910000022
represent the similarity probability between the partial structurtes of reference image vegetarian refreshments default in the 1st partial structurtes that low-resolution pixel is selected and selected window,
Figure FDA0000376735910000023
represent the similarity probability i≤m between the partial structurtes of reference image vegetarian refreshments default in partial structurtes that i low-resolution pixel selected and selected window,
Figure FDA0000376735910000024
represent the similarity probability between the partial structurtes of reference image vegetarian refreshments default in partial structurtes that m low-resolution pixel selected and selected window; And
Utilize the low resolution neighbours' point in four diagonals of full-resolution picture vegetarian refreshments, similarity probability between the partial structurtes of default reference image vegetarian refreshments in the partial structurtes of each full-resolution picture vegetarian refreshments and selected window in the selected window of calculating, and form full-resolution picture vegetarian refreshments probability vector P h, wherein
Figure FDA0000376735910000025
Figure FDA0000376735910000026
represent the similarity probability between the partial structurtes of reference image vegetarian refreshments default in the partial structurtes of the 1st full-resolution picture vegetarian refreshments and selected window,
Figure FDA0000376735910000027
represent the similarity probability k≤n between the partial structurtes of reference image vegetarian refreshments default in the partial structurtes of k full-resolution picture vegetarian refreshments and selected window,
Figure FDA0000376735910000028
represent the similarity probability between the partial structurtes of reference image vegetarian refreshments default in the partial structurtes of n full-resolution picture vegetarian refreshments and selected window.
4. method as claimed in claim 3, is characterized in that, according to the pixel value of known low-resolution pixel point in selected window, the first parameter and the second parameter is estimated to be specially:
Directly pass through the first parameter is weighted to least-squares estimation, wherein
Figure FDA00003767359100000210
represent the first parameter optimal value estimated result, a trepresent a vectorial composition value by the first parameter of vector representation, t ∈ { 1,2,3,4}, f (x i) represent i low-resolution pixel point x ipixel value,
Figure FDA0000376735910000031
represent pixel x it low resolution neighbours point in diagonal
Figure FDA0000376735910000032
pixel value;
Directly pass through
Figure FDA0000376735910000033
the second parameter is weighted to least square method and estimates, wherein
Figure FDA0000376735910000034
represent the second parameter optimal value estimated result, b trepresent a vectorial composition value by the second parameter of vector representation,
Figure FDA0000376735910000035
represent low-resolution pixel x jt neighbours' point in vertical and horizontal line direction
Figure FDA0000376735910000036
pixel value.
5. method as claimed in claim 4, is characterized in that, sets up optimization aim function be specially according to similarity probability and matching error:
Set up optimization aim function
Figure FDA0000376735910000037
meet following constraint condition simultaneously
Figure FDA0000376735910000038
, wherein F is optimization aim function, and f (y) represents the pixel value of full-resolution picture vegetarian refreshments, and w represents selected window,
Figure FDA0000376735910000039
k '=1,2 ..., Q.,
Figure FDA00003767359100000310
expression is passed through
Figure FDA00003767359100000311
portray full-resolution picture vegetarian refreshments y k 'with neighbor pixel point in its diagonal
Figure FDA00003767359100000312
matching error during regression relation, Q represents to have neighbor pixel point in selected window inner opposite angle line direction
Figure FDA00003767359100000313
full-resolution picture vegetarian refreshments y k 'number,
Figure FDA00003767359100000314
i '=1,2 ..., P.,
Figure FDA00003767359100000315
expression is passed through portray low-resolution pixel point x i 'with neighbor pixel point in its diagonal
Figure FDA00003767359100000317
matching error during regression relation, P represents to have neighbor pixel point in selected window inner opposite angle line direction
Figure FDA00003767359100000318
low-resolution pixel point x i 'number,
Figure FDA00003767359100000319
k "=1,2 ..., K.,
Figure FDA00003767359100000320
expression is passed through
Figure FDA00003767359100000321
portray full-resolution picture vegetarian refreshments y k "with neighbor pixel point in its diagonal
Figure FDA00003767359100000322
matching error during regression relation, K represents to have neighbor pixel point in vertical in selected window and horizontal line direction
Figure FDA00003767359100000323
full-resolution picture vegetarian refreshments y k "number,
Figure FDA00003767359100000324
i "=1,2 ..., S., expression is passed through portray low-resolution pixel point x i "with neighbor pixel point in its diagonal
Figure FDA00003767359100000326
matching error during regression relation, S represents to have neighbor pixel point in vertical in selected window and horizontal line direction
Figure FDA00003767359100000327
low-resolution pixel point x i "number.
6. an image interpolation device that improves image resolution ratio, is characterized in that, comprising:
Probability generation module, for the similarity probability between the partial structurtes of the partial structurtes of each pixel in the selected window of computed image and the default reference image vegetarian refreshments of selected window;
The first estimation module, for the first parameter and the second parameter being estimated according to the pixel value of known low-resolution pixel point in selected window, the first parameter is for portraying in window the model parameter of neighbor pixel point regression relation on each pixel and its diagonal, the second parameter be for portray interior each pixel of window perpendicular with horizontal line direction on the model parameter of neighbor pixel point regression relation;
Correlationship is set up module, for utilizing the estimated result of the pixel value of low-resolution pixel point, the estimated result of the first parameter and the second parameter, sets up the pixel value of interpolation full-resolution picture vegetarian refreshments in selected window and the correlationship of matching error;
The second estimation module, for setting up optimization aim function according to similarity probability and matching error, carries out to optimization aim function the pixel value that optimum estimate obtains full-resolution picture vegetarian refreshments;
Insert module, for the pixel value of the full-resolution picture vegetarian refreshments that obtains by optimum estimate, the full-resolution picture vegetarian refreshments that similarity probability is greater than to setting threshold inserts selected window; And if reference image vegetarian refreshments is full-resolution picture vegetarian refreshments, the full-resolution picture vegetarian refreshments as reference image vegetarian refreshments is inserted to selected window.
7. device as claimed in claim 6, is characterized in that, probability generation module, also for passing through
Figure FDA0000376735910000041
carry out similarity probability calculation, in the partial structurtes that wherein p (d, j) is j pixel in selected window and selected window as the similarity probability between the partial structurtes of d pixel of reference image vegetarian refreshments,
Figure FDA0000376735910000042
Figure FDA0000376735910000043
be the vector of the pixel value formation of ordering of the neighbours in four diagonals of d pixel in selected window, d is positive integer, d≤m+n, n is the number of selected window middle high-resolution pixel, m is the number of low-resolution pixel point, and ε is one and avoids the positive number except zero overflow error
Figure FDA0000376735910000044
Figure FDA0000376735910000045
be the vector of the pixel value formation of ordering of the neighbours in four diagonals of j pixel in selected window, j is positive integer, j≤m+n, and h is used for the distribution of shapes parameter of control characteristic function.
8. device as claimed in claim 7, it is characterized in that, probability generation module, also for utilizing the low resolution neighbours' point in four diagonals of low-resolution pixel point, similarity probability in the partial structurtes that in the selected window of calculating, each known low-resolution pixel is selected and selected window between the partial structurtes of default reference image vegetarian refreshments, and form low-resolution pixel point similarity probability vector P l, wherein
Figure FDA0000376735910000051
Figure FDA0000376735910000052
represent the similarity probability between the partial structurtes of reference image vegetarian refreshments default in the 1st partial structurtes that low-resolution pixel is selected and selected window,
Figure FDA0000376735910000053
represent the similarity probability i≤m between the partial structurtes of reference image vegetarian refreshments default in partial structurtes that i low-resolution pixel selected and selected window,
Figure FDA0000376735910000054
represent the similarity probability between the partial structurtes of reference image vegetarian refreshments default in partial structurtes that m low-resolution pixel selected and selected window; And
Utilize the low resolution neighbours' point in four diagonals of full-resolution picture vegetarian refreshments, similarity probability between the partial structurtes of default reference image vegetarian refreshments in the partial structurtes of each full-resolution picture vegetarian refreshments and selected window in the selected window of calculating, and form full-resolution picture vegetarian refreshments probability vector P h, wherein
Figure FDA0000376735910000055
Figure FDA0000376735910000056
represent the similarity probability between the partial structurtes of reference image vegetarian refreshments default in the partial structurtes of the 1st full-resolution picture vegetarian refreshments and selected window,
Figure FDA0000376735910000057
represent the similarity probability k≤n between the partial structurtes of reference image vegetarian refreshments default in the partial structurtes of k full-resolution picture vegetarian refreshments and selected window,
Figure FDA0000376735910000058
represent the similarity probability between the partial structurtes of reference image vegetarian refreshments default in the partial structurtes of n full-resolution picture vegetarian refreshments and selected window.
9. device as claimed in claim 8, is characterized in that, the first estimation module, also for directly passing through
Figure FDA0000376735910000059
the first parameter is weighted to least-squares estimation, wherein
Figure FDA00003767359100000510
represent the first parameter optimal value estimated result, a trepresent a vectorial composition value by the first parameter of vector representation, t ∈ { 1,2,3,4}, f (x i) represent i low-resolution pixel point x ipixel value,
Figure FDA00003767359100000511
represent pixel x it low resolution neighbours point in diagonal
Figure FDA00003767359100000512
pixel value;
Directly pass through
Figure FDA00003767359100000514
the second parameter is weighted to least square method and estimates, wherein
Figure FDA00003767359100000513
represent the second parameter optimal value estimated result, b trepresent a vectorial composition value by the second parameter of vector representation,
Figure FDA0000376735910000061
represent low-resolution pixel x it neighbours' point in vertical and horizontal line direction
Figure FDA0000376735910000062
pixel value.
10. device as claimed in claim 9, is characterized in that, the second estimation module, also for setting up optimization aim function meet following constraint condition simultaneously
Figure FDA0000376735910000064
, wherein F is optimization aim function, and f (y) represents the pixel value of full-resolution picture vegetarian refreshments, and w represents selected window,
Figure FDA0000376735910000065
k '=1,2 ..., Q.,
Figure FDA0000376735910000066
expression is passed through
Figure FDA0000376735910000067
portray full-resolution picture vegetarian refreshments y k 'with neighbor pixel point in its diagonal
Figure FDA0000376735910000068
matching error during regression relation, Q represents to have neighbor pixel point in selected window inner opposite angle line direction
Figure FDA0000376735910000069
full-resolution picture vegetarian refreshments y k 'number,
Figure FDA00003767359100000610
i'=1,2 ..., P.,
Figure FDA00003767359100000611
expression is passed through
Figure FDA00003767359100000612
portray low-resolution pixel point x i 'with neighbor pixel point in its diagonal
Figure FDA00003767359100000613
matching error during regression relation, P represents to have neighbor pixel point in selected window inner opposite angle line direction
Figure FDA00003767359100000614
low-resolution pixel point x i 'number,
Figure FDA00003767359100000615
k "=1,2 ..., K.,
Figure FDA00003767359100000616
expression is passed through
Figure FDA00003767359100000617
portray full-resolution picture vegetarian refreshments y k "with neighbor pixel point in its diagonal
Figure FDA00003767359100000618
matching error during regression relation, K represents to have neighbor pixel point in vertical in selected window and horizontal line direction
Figure FDA00003767359100000619
full-resolution picture vegetarian refreshments y k "number,
Figure FDA00003767359100000620
i "=1,2 ..., S.,
Figure FDA00003767359100000621
expression is passed through
Figure FDA00003767359100000622
portray low-resolution pixel point x i "with neighbor pixel point in its diagonal
Figure FDA00003767359100000623
matching error during regression relation, S represents to have neighbor pixel point in vertical in selected window and horizontal line direction
Figure FDA00003767359100000624
low-resolution pixel point x i "number.
CN201010558745.1A 2010-11-25 2010-11-25 Image interpolation method capable of improving image resolution and device utilizing image interpolation method Expired - Fee Related CN102479380B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201010558745.1A CN102479380B (en) 2010-11-25 2010-11-25 Image interpolation method capable of improving image resolution and device utilizing image interpolation method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201010558745.1A CN102479380B (en) 2010-11-25 2010-11-25 Image interpolation method capable of improving image resolution and device utilizing image interpolation method

Publications (2)

Publication Number Publication Date
CN102479380A CN102479380A (en) 2012-05-30
CN102479380B true CN102479380B (en) 2014-01-15

Family

ID=46092013

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201010558745.1A Expired - Fee Related CN102479380B (en) 2010-11-25 2010-11-25 Image interpolation method capable of improving image resolution and device utilizing image interpolation method

Country Status (1)

Country Link
CN (1) CN102479380B (en)

Families Citing this family (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102946522B (en) * 2012-10-22 2015-08-19 华为技术有限公司 Based on super-resolution method and the equipment of non-local mean
CN103854254B (en) 2012-11-28 2016-12-21 北京大学 A kind of arbitrary proportion image interpolation method based on autoregression model
CN103179424B (en) * 2013-03-26 2015-03-11 深圳市掌网立体时代视讯技术有限公司 Method and device for synthesising three-dimensional image
CN104715450A (en) * 2013-12-16 2015-06-17 方正国际软件(北京)有限公司 Method and system for extracting icons from picture
CN105096247B (en) * 2014-05-09 2018-02-06 北京大学 Image interpolation method and device
CN105069747B (en) * 2015-07-13 2018-07-20 中国科学院遥感与数字地球研究所 Image interpolation method and device
CN106558024B (en) * 2015-09-28 2019-02-01 北京大学 Carry out the autoregression interpolation method and device of self-adapting window expansion
CN105719238B (en) * 2016-01-15 2018-11-02 电子科技大学 A kind of image interpolation method based on non local geometric similarity
CN112416915A (en) * 2020-11-11 2021-02-26 北京大学 Data correction method, device, storage medium and equipment
CN113850719B (en) * 2021-06-16 2024-06-18 北京大学 RGB image guided depth map super-resolution method based on joint implicit image function
CN113643341B (en) * 2021-10-12 2021-12-28 四川大学 Different-scale target image registration method based on resolution self-adaptation

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101226634A (en) * 2008-01-30 2008-07-23 哈尔滨工业大学 Method for reestablishment of single frame image quick super-resolution based on nucleus regression
CN101668196A (en) * 2009-09-25 2010-03-10 西安电子科技大学 Low code rate image compression method based on down sampling and interpolation
CN101674478A (en) * 2009-10-19 2010-03-17 浙江大学 Video super-resolution method based on compressed domain

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6771835B2 (en) * 2000-06-12 2004-08-03 Samsung Electronics Co., Ltd. Two-dimensional non-linear interpolation system based on edge information and two-dimensional mixing interpolation system using the same

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101226634A (en) * 2008-01-30 2008-07-23 哈尔滨工业大学 Method for reestablishment of single frame image quick super-resolution based on nucleus regression
CN101668196A (en) * 2009-09-25 2010-03-10 西安电子科技大学 Low code rate image compression method based on down sampling and interpolation
CN101674478A (en) * 2009-10-19 2010-03-17 浙江大学 Video super-resolution method based on compressed domain

Also Published As

Publication number Publication date
CN102479380A (en) 2012-05-30

Similar Documents

Publication Publication Date Title
CN102479380B (en) Image interpolation method capable of improving image resolution and device utilizing image interpolation method
CN111739078B (en) Monocular unsupervised depth estimation method based on context attention mechanism
US9524539B2 (en) Method for edge-directed adaptive image interpolation and VLSI implementation device for the same
CN107578430B (en) Stereo matching method based on self-adaptive weight and local entropy
US7043091B2 (en) Method and apparatus for increasing spatial resolution of an image
Yifan et al. Iso-points: Optimizing neural implicit surfaces with hybrid representations
CN111899295B (en) Monocular scene depth prediction method based on deep learning
CN106991686B (en) A kind of level set contour tracing method based on super-pixel optical flow field
CN106204454B (en) High-precision rapid image interpolation method based on texture edge self-adaption data fusion
CN103455991A (en) Multi-focus image fusion method
CN107403414A (en) A kind of image area selecting method and system for being beneficial to fuzzy kernel estimates
CN110415169B (en) Depth map super-resolution reconstruction method, system and electronic equipment
CN115601406A (en) Local stereo matching method based on fusion cost calculation and weighted guide filtering
CN109741358B (en) Superpixel segmentation method based on adaptive hypergraph learning
CN113034387B (en) Image denoising method, device, equipment and medium
CN111369435B (en) Color image depth up-sampling method and system based on self-adaptive stable model
Zhang et al. Nonlocal edge-directed interpolation
CN106056575B (en) A kind of image matching method based on like physical property proposed algorithm
CN106611405B (en) Image interpolation method and device
Yang et al. Hierarchical joint bilateral filtering for depth post-processing
CN108364255A (en) Remote sensing image amplification method based on rarefaction representation and Partial Differential Equation Model
CN103618904A (en) Motion estimation method and device based on pixels
Jini et al. Image Inpainting Using Image Interpolation-An Analysis
Gong et al. Multi-view stereo point clouds visualization
Li et al. Quantitative evaluation for dehazing algorithms on synthetic outdoor hazy dataset

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
C14 Grant of patent or utility model
GR01 Patent grant
TR01 Transfer of patent right

Effective date of registration: 20220623

Address after: 100871 No. 5, the Summer Palace Road, Beijing, Haidian District

Patentee after: Peking University

Patentee after: New founder holdings development Co.,Ltd.

Patentee after: BEIJING FOUNDER ELECTRONICS Co.,Ltd.

Address before: 100871 No. 5, the Summer Palace Road, Beijing, Haidian District

Patentee before: Peking University

Patentee before: PEKING UNIVERSITY FOUNDER GROUP Co.,Ltd.

Patentee before: BEIJING FOUNDER ELECTRONICS Co.,Ltd.

TR01 Transfer of patent right
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20140115

CF01 Termination of patent right due to non-payment of annual fee