CN107123094A - A kind of mixing Poisson, the video denoising method of gaussian sum impulsive noise - Google Patents
A kind of mixing Poisson, the video denoising method of gaussian sum impulsive noise Download PDFInfo
- Publication number
- CN107123094A CN107123094A CN201710179189.9A CN201710179189A CN107123094A CN 107123094 A CN107123094 A CN 107123094A CN 201710179189 A CN201710179189 A CN 201710179189A CN 107123094 A CN107123094 A CN 107123094A
- Authority
- CN
- China
- Prior art keywords
- block
- mrow
- noise
- video
- denoising
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 46
- 238000002156 mixing Methods 0.000 title claims abstract description 15
- 239000011159 matrix material Substances 0.000 claims description 45
- 239000013598 vector Substances 0.000 claims description 34
- 238000010276 construction Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 abstract description 2
- 230000000694 effects Effects 0.000 description 16
- 230000003068 static effect Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 241000196324 Embryophyta Species 0.000 description 1
- 235000008331 Pinus X rigitaeda Nutrition 0.000 description 1
- 235000011613 Pinus brutia Nutrition 0.000 description 1
- 241000018646 Pinus brutia Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20021—Dividing image into blocks, subimages or windows
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a kind of video denoising method for mixing Poisson, gaussian sum impulsive noise, it is related to image processing field, including：The each two field picture of noise video is divided into image block first, by block matching method to each image block search similar image block in noise sequence of frames of video；Then Poisson Gaussian mixed noise priori, the video denoising model set up under Poisson, gaussian sum pulse mixed noise environment are constructed；Denoising model is finally solved using optimized algorithm, the picture rich in detail block after denoising, and the clear video image after generation denoising on this basis is obtained.This method is different from conventional video denoising method just for certain type of noise, this method is for mixing Poisson, the video denoising of gaussian sum impulsive noise, the video denoising under actual noise environment can be preferably solved the problems, such as, and results in compared with traditional video denoising algorithm higher-quality video denoising image.
Description
Technical field：
Present invention relates generally to image and video information process field, refer in particular to a kind of mixing Poisson, gaussian sum pulse and make an uproar
The video denoising method of sound.
Background technology：
Although digital camera and digital sensor are developed rapidly in recent years, in acquisition of information and transmitting procedure
In, video data is often subject to the influence of a variety of noises.Noise in vision signal can cause the sense of discomfort in visual effect, and
And [1] such as the performance of some follow-up video processnig algorithms, including feature extraction, target detection and motion trackings can be influenceed.Cause
This, video denoising is still an active research direction, and with network cameras and mobile phone camera a large amount of uses and body
Reveal prior meaning.In order to recover raw information, many scholar's designs from noise pollution and the video data degraded
Various video Denoising Algorithm is used for solving this problem [2-4].Due to the high acquisition rate of digital camera, the quality of video data
Often below static single image data, its quality problems include low signal-to-noise ratio and low resolution etc..Meanwhile, video data phase
Than having higher space-time redundancy for static image data, more rich scene letter can be provided than static image data
Breath.Therefore to the restoration algorithm effect for the video that degrades, can depend greatly on this space-time redundancy by effective profit
With, and any degree can be excavated.
In the imaging system of reality, in addition to it can produce noise similar to the internal system of white Gaussian noise, also
Poisson and impulsive noise are there is, wherein poisson noise embodies the fluctuation [5] in photon counting, and impulsive noise then embodies sensing
The collection of device signal and transmission error.Conventional video Denoising Algorithm mainly handles single type noise, for mixed noise denoising
Effect is often undesirable.Although the processing of Poisson, Gauss and pulse mixed noise is significant in actual environment,
Research in terms of such mixed noise video denoising algorithm is still very limited.
The present invention provides a kind of mixing Poisson, the video denoising method of gaussian sum impulsive noise, can be more reasonably to this
Plant mixed noise to be modeled and handle, can be obtained than existing method more preferably denoising effect for this kind of mixed noise,
There is important theory and practice meaning for the video denoising under Poisson, gaussian sum pulse mixed noise environment.
[1]A.Bovik,Handbook of Image and Video Processing,Academic Press,San
Diego,CA,2000.
[2]K.Dabov,A.Foi,and K.Egiazarian,Video denoising by sparse 3-D
transform-domain collaborative filtering,IEEE trans.Eur.Signal Process.Conf.,
Poznan,Poland,1257-1260,Sep.2007.
[3]Maggioni M,Boracchi G,Foi A,et al.Video denoising,deblocking,and
enhancement through separable 4-d nonlocal spatiotemporal transforms.IEEE
Transactions on image processing,2012,21(9):3952-3966.
[4]Hui Ji,Sibin Huang,Zuowei Shen,and Yuhong Xu,Robust Video
Restoration by Joint Sparse and Low Rank Matrix Approximation,SIAM J.IMAGING
SCIENCES,4(4),pp.1122-1142,2011.
[5]D.L.Snyder,A.M.Hammoud,and R.L.White.Image recovery from data
acquired with a charge-coupled-device camera.J.Opt.Soc.Am.A,10(1983),1014-
1023.
[6]Z.Shen,K.-C.Toh,and S.Yun,An accelerated proximal gradient
algorithm for frame-based image restoration via the balanced approach,SIAM
J.Imaging Sci.,4(2011),573-596.
The content of the invention：
The present invention for conventional video Denoising Algorithm mainly handle single type noise and for mixed noise denoising effect
Undesirable the problem of, a kind of mixing Poisson, the video denoising method of gaussian sum impulsive noise are disclosed.
In order to solve the above technical problems, technical scheme proposed by the present invention is：
A kind of mixing Poisson, the video denoising method of gaussian sum impulsive noise, it is characterised in that：
Step one：The each two field picture of noise video is divided into image block, Block- matching is passed through in noise sequence of frames of video
Method is to each image block search similar image block.The each two field picture of noise video is divided into the specific method of image block
It is：
For noise sequence of frames of video { I1,I2,…,In(n be noise video frame number), by each two field picture be divided into s ×
Overlapping region width is w between the image block of s pixel sizes, image blockoIndividual pixel.For each image block P, pass through block
Method of completing the square searches for similar image block in noise sequence of frames of video, and obtains m similar image block in each frame；For each
Its all row is in turn connected to form a column vector, then the column vector collection of all similar blocks is combined into by similar image block：{di}(i
=1,2 ..., mn).On this basis, observation block matrix is constituted with all similar block column vectorsWherein D's is each
Row are all a similar block column vector, Rh×wIt is the real number matrix set that h, columns are w to represent line number.
Image block matching method in the step one, is including but not limited to entered based on Euclidean distance between image block vector
The method of row image Block- matching；
Step 2：Construct Poisson-Gaussian mixed noise priori；Its concrete form is：
Wherein, F (A, E, Z) represents Poisson-Gaussian mixed noise priori；A is the block matrix that clear block column vector is constituted, A
Each be classified as the corresponding clear block column vector of foregoing similar block；E represents block-matching error block matrix, and each of E is classified as foregoing
The corresponding matching error column vector of similar block；Z represents impulsive noise matrix, and each of Z is classified as the corresponding pulse of foregoing similar block
Noise vector；D represents the observation block matrix that similar block column vector is constituted；Max (X) representing matrixs X greatest member, division arithmetic
It is that matrix is carried out by element with square root calculation；σ is mixes in Poisson, gaussian sum poisson noise, the standard of Gaussian noise part
Difference.And N all elements are all 1.
Step 3：The video denoising model that construction is set up under mixing Poisson, gaussian sum impulse noise environment；Concrete form
For：
Solve denoising block matrix A, the error block matrix E and impulsive noise matrix Z for causing target function value to reach minimum：
Wherein, A is the block matrix that clear block column vector is constituted, and E represents block-matching error block matrix, and Z represents impulsive noise
Matrix, F (A, E, Z) represents Poisson-Gaussian mixed noise priori, | | | |*, | | | |1With | | | |FRespectively matrix core model
Number, l1Norm and Frobenius norms, λ, μ and η are constant coefficient.After solving picture rich in detail block matrix A, A row to
Amount is remapped as s × s image blocks, obtains the corresponding picture rich in detail block of similar image block；
Step 4：Denoising model is solved using optimized algorithm, the picture rich in detail block after denoising is obtained；Solve picture rich in detail block
Optimized algorithm, include but are not limited to existing algorithm Accelerated Proximal Gradient (APG).
Step 5：Picture rich in detail merged block after denoising is generated into clear video image.Its specific method is：
For kth frame (k=1,2 ..., n) clear video image, if including pixel pkThe picture rich in detail set of blocks of (x, y)
For { b1,b2,…,bv}.Wherein, x, y are respectively line number and columns of the pixel in video image, pk(x, y) is (x, y) place
Pixel, v is includes pixel pkThe image block number of (x, y), { b1,b2,…,bvRepresent to include pixel pk(x's, y) is clear
Image block set.Then pixel pkThe gray value of (x, y) is：
Wherein, I (pk(x, y)) it is pixel pkThe gray value of (x, y),For bj(j=1,2 ..., v) in pixel
pk(x, y) corresponding gray value, v is to include pixel pkThe image block number of (x, y).
Beneficial effects of the present invention：(1) Poisson, the situation of three kinds of noise mixing of gaussian sum pulse are considered, and reasonably
Mixed model is modeled.Certain type of noise or a small amount of mixed noise are only considered in the existing method that compares, the present invention
Model with close to real video noise situation.(2) present invention proposes the video denoising method for three kinds of mixed noises, phase
Existing mixing denoising method is contrasted, final denoising effect of the invention is more preferably obvious, and denoising video quality is higher.
Brief description of the drawings：
Fig. 1 is the flow chart of the method for the invention；
Fig. 2 is the present invention and the visual effect comparison diagram of other video denoising methods；In Fig. 2, the 1st row is clear noiseless
Video image；2nd row is the noise video image added after Poisson, gaussian sum impulsive noise；3rd row is VBM3D algorithms [2]
Denoising effect；4th row is VBM4D algorithms [3] denoising effect；5th row is RPCA algorithms [4] denoising effect；6th row is this method
Denoising effect；
In the case of Fig. 3 is different noise level and experiment test videos, the present invention and the PSNR of other video denoising methods
Index comparing result.
Embodiment：
Below in conjunction with the accompanying drawings 1, the present invention will be described in detail：
A kind of mixing Poisson of the present embodiment offer, the video denoising method of gaussian sum impulsive noise, comprise the following steps：
Step one：The each two field picture of noise video is divided into image block, Block- matching is passed through in noise sequence of frames of video
Method is to each image block search similar image block.The each two field picture of noise video is divided into the specific method of image block
It is：
For noise sequence of frames of video { I1,I2,…,In(n be noise video frame number), by each two field picture be divided into s ×
Overlapping region width is w between the image block of s pixel sizes, image blockoIndividual pixel.For each image block P, pass through block
Method of completing the square searches for similar image block in noise sequence of frames of video, and obtains m similar image block in each frame；For each
Its all row is in turn connected to form a column vector, then the column vector collection of all similar blocks is combined into by similar image block：{di}(i
=1,2 ..., mn).On this basis, observation block matrix is constituted with all similar block column vectorsWherein D's is each
Row are all a similar block column vector, Rh×wIt is the real number matrix set that h, columns are w to represent line number.
Image block matching method in the step one, is including but not limited to entered based on Euclidean distance between image block vector
The method of row image Block- matching；
Step 2：Construct Poisson-Gaussian mixed noise priori；Its concrete form is：
Wherein, F (A, E, Z) represents Poisson-Gaussian mixed noise priori；A is the block matrix that clear block column vector is constituted, A
Each be classified as the corresponding clear block column vector of foregoing similar block；E represents block-matching error block matrix, and each of E is classified as foregoing
The corresponding matching error column vector of similar block；Z represents impulsive noise matrix, and each of Z is classified as the corresponding pulse of foregoing similar block
Noise vector；D represents the observation block matrix that similar block column vector is constituted；Max (X) representing matrixs X greatest member, division arithmetic
It is that matrix is carried out by element with square root calculation；σ is mixes in Poisson, gaussian sum poisson noise, the standard of Gaussian noise part
Difference.And N all elements are all 1.
Step 3：The video denoising model that construction is set up under mixing Poisson, gaussian sum impulse noise environment；Concrete form
For：
Solve denoising block matrix A, the error block matrix E and impulsive noise matrix Z for causing target function value to reach minimum：
Wherein, A is the block matrix that clear block column vector is constituted, and E represents block-matching error block matrix, and Z represents impulsive noise
Matrix, F (A, E, Z) represents Poisson-Gaussian mixed noise priori, | | | |*, | | | |1With | | | |FRespectively matrix core model
Number, l1Norm and Frobenius norms, λ, μ and η are constant coefficient.After solving picture rich in detail block matrix A, A row to
Amount is remapped as s × s image blocks, obtains the corresponding picture rich in detail block of similar image block；
Step 4：Denoising model is solved using optimized algorithm, the picture rich in detail block after denoising is obtained；Solve picture rich in detail block
Optimized algorithm, include but are not limited to existing algorithm Accelerated Proximal Gradient (APG).
Step 5：Picture rich in detail merged block after denoising is generated into clear video image.Its specific method is：
For kth frame (k=1,2 ..., n) clear video image, if including pixel pkThe picture rich in detail set of blocks of (x, y)
For { b1,b2,…,bv}.Wherein, x, y are respectively line number and columns of the pixel in video image, pk(x, y) is (x, y) place
Pixel, v is includes pixel pkThe image block number of (x, y), { b1,b2,…,bvRepresent to include pixel pk(x's, y) is clear
Image block set.Then pixel pkThe gray value of (x, y) is：
Wherein, I (pk(x, y)) it is pixel pkThe gray value of (x, y),For bj(j=1,2 ..., v) in pixel
pk(x, y) corresponding gray value, v is to include pixel pkThe image block number of (x, y).
In concrete operations, using bus, coastguard, flower, missa and salesman video sequences [4] are carried out
Experimental evaluation.Poisson noise, Gaussian noise and obedience [0-255] equally distributed pulse is added on clear video frame images to make an uproar
Sound, then carries out denoising to noise frame of video, examines this method effect, and with the video denoising algorithm that is widely used at present：
VBM3D [2], VBM4D [3] and RPCA [4] are compared.Wherein, denoising after-vision effect (Gaussian noise relatively more as shown in Figure 2
Standard deviation is σ=20, and impulsive noise ratio is r=0.1), be respectively from left to right：Clear noiseless video image；Add pool
Noise video image after pine, gaussian sum impulsive noise；VBM3D algorithm denoising effects；VBM4D algorithm denoising effects；RPCA
Algorithm denoising effect；This method denoising effect.PSNR Indexes Comparisons after denoising are as shown in figure 3, wherein Gaussian noise standard deviation sigma is
20-40, impulsive noise ratio r are 10%-30%.By contrast as can be seen that several videos of this method and current main flow are gone
Method for de-noising, which is compared, can obtain more preferably mixed noise denoising effect.
One of ordinary skill in the art will appreciate that realizing all or part of flow in above-described embodiment method.In above
Hold a kind of described only preferred embodiment of the invention, the interest field of the present invention can not be limited with this certainly, because
This equivalent variations made according to the claims in the present invention, still belongs to the scope that the present invention is covered.
Claims (1)
1. a kind of mixing Poisson, the video denoising method of gaussian sum impulsive noise, it is characterised in that comprise the following steps：
Step one：The each two field picture of noise video is divided into image block, block matching method is passed through in noise sequence of frames of video
To each image block search similar image block；
The specific method that each two field picture of noise video is divided into image block is：
For noise sequence of frames of video { I1,I2,…,In(n is noise video frame number), each two field picture is divided into s × s pictures
Overlapping region width is w between the image block of plain size, image blockoIndividual pixel.For each image block P, pass through Block- matching
Method searches for similar image block in noise sequence of frames of video, and obtains m similar image block in each frame；For each phase
Like image block, its all row is in turn connected to form a column vector, then the column vector collection of all similar blocks is combined into：{di(i=
1,2,…,mn).On this basis, observation block matrix is constituted with all similar block column vectorsWherein D each row
All it is a similar block column vector, Rh×wIt is the real number matrix set that h, columns are w to represent line number；
Image block matching method in the step one, is including but not limited to schemed based on Euclidean distance between image block vector
As the method for Block- matching；
Step 2：Construct Poisson-Gaussian mixed noise priori；Its concrete form is：
<mrow>
<mi>F</mi>
<mrow>
<mo>(</mo>
<mi>A</mi>
<mo>,</mo>
<mi>E</mi>
<mo>,</mo>
<mi>Z</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mo>|</mo>
<mo>|</mo>
<mfrac>
<mrow>
<mo>(</mo>
<mi>D</mi>
<mo>-</mo>
<mi>A</mi>
<mo>-</mo>
<mi>E</mi>
<mo>-</mo>
<mi>Z</mi>
<mo>)</mo>
<mi>max</mi>
<mo>(</mo>
<msqrt>
<mrow>
<mi>A</mi>
<mo>+</mo>
<mi>E</mi>
<mo>+</mo>
<msup>
<mi>&sigma;</mi>
<mn>2</mn>
</msup>
<mi>N</mi>
</mrow>
</msqrt>
<mo>)</mo>
</mrow>
<msqrt>
<mrow>
<mi>A</mi>
<mo>+</mo>
<mi>E</mi>
<mo>+</mo>
<msup>
<mi>&sigma;</mi>
<mn>2</mn>
</msup>
<mi>N</mi>
</mrow>
</msqrt>
</mfrac>
<mo>|</mo>
<msubsup>
<mo>|</mo>
<mi>F</mi>
<mn>2</mn>
</msubsup>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, F (A, E, Z) represents Poisson-Gaussian mixed noise priori；A is the block matrix that clear block column vector is constituted, and A's is every
One is classified as the corresponding clear block column vector of foregoing similar block；E represents block-matching error block matrix, and each of E is classified as foregoing similar
The corresponding matching error column vector of block；Z represents impulsive noise matrix, and each of Z is classified as the corresponding impulsive noise of foregoing similar block
Vector；D represents the observation block matrix that similar block column vector is constituted；Max (X) representing matrixs X greatest member, division arithmetic peace
Root operation is that matrix is carried out by element；σ is mixes in Poisson, gaussian sum poisson noise, the standard deviation of Gaussian noise part.And N all elements are all 1；
Step 3：The video denoising model that construction is set up under mixing Poisson, gaussian sum impulse noise environment；Concrete form is：
Solve denoising block matrix A, the error block matrix E and impulsive noise matrix Z for causing target function value to reach minimum：
<mrow>
<munder>
<mi>min</mi>
<mrow>
<mi>A</mi>
<mo>,</mo>
<mi>E</mi>
<mo>,</mo>
<mi>Z</mi>
</mrow>
</munder>
<mo>|</mo>
<mo>|</mo>
<mi>A</mi>
<mo>|</mo>
<msub>
<mo>|</mo>
<mo>*</mo>
</msub>
<mo>+</mo>
<mi>&lambda;</mi>
<mo>|</mo>
<mo>|</mo>
<mi>E</mi>
<mo>|</mo>
<msub>
<mo>|</mo>
<mn>1</mn>
</msub>
<mo>+</mo>
<mi>&mu;</mi>
<mo>|</mo>
<mo>|</mo>
<mi>Z</mi>
<mo>|</mo>
<msub>
<mo>|</mo>
<mn>1</mn>
</msub>
<mo>+</mo>
<mfrac>
<mn>1</mn>
<mrow>
<mn>2</mn>
<mi>&eta;</mi>
</mrow>
</mfrac>
<mi>F</mi>
<mrow>
<mo>(</mo>
<mi>A</mi>
<mo>,</mo>
<mi>E</mi>
<mo>,</mo>
<mi>Z</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, A is the block matrix that clear block column vector is constituted, and E represents block-matching error block matrix, and Z represents impulsive noise matrix,
F (A, E, Z) represents Poisson-Gaussian mixed noise priori, | | | |*, | | | |1With | | | |FRespectively matrix nuclear norm,
Norm and Frobenius norms, λ, μ and η are constant coefficient.After solving picture rich in detail block matrix A, A column vector weight
S × s image blocks newly are transformed to, the corresponding picture rich in detail block of similar image block is obtained；
Step 4：Denoising model is solved using optimized algorithm, the picture rich in detail block after denoising is obtained；Solve the excellent of picture rich in detail block
Change algorithm, include but are not limited to existing algorithm Accelerated Proximal Gradient (APG)；
Step 5：Picture rich in detail merged block after denoising is generated into clear video image, specific method is：
For kth frame (k=1,2 ..., n) clear video image, if including pixel pkThe picture rich in detail set of blocks of (x, y) is { b1,
b2,…,bv}.Wherein, x, y are respectively line number and columns of the pixel in video image, pk(x, y) is the pixel at (x, y) place
Point, v is to include pixel pkThe image block number of (x, y), { b1,b2,…,bvRepresent to include pixel pkThe picture rich in detail block of (x, y)
Set.Then pixel pkThe gray value of (x, y) is：
<mrow>
<mi>I</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>p</mi>
<mi>k</mi>
</msub>
<mo>(</mo>
<mrow>
<mi>x</mi>
<mo>,</mo>
<mi>y</mi>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mi>v</mi>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>v</mi>
</munderover>
<mi>I</mi>
<mrow>
<mo>(</mo>
<msubsup>
<mi>p</mi>
<mi>k</mi>
<mi>j</mi>
</msubsup>
<mo>(</mo>
<mrow>
<mi>x</mi>
<mo>,</mo>
<mi>y</mi>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, I (pk(x, y)) it is pixel pkThe gray value of (x, y),For bj(j=1,2 ..., v) in pixel pk(x,
Y) corresponding gray value, v is to include pixel pkThe image block number of (x, y).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710179189.9A CN107123094B (en) | 2017-03-23 | 2017-03-23 | Video denoising method mixing Poisson, Gaussian and impulse noise |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710179189.9A CN107123094B (en) | 2017-03-23 | 2017-03-23 | Video denoising method mixing Poisson, Gaussian and impulse noise |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107123094A true CN107123094A (en) | 2017-09-01 |
| CN107123094B CN107123094B (en) | 2020-07-17 |
Family
ID=59718122
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710179189.9A Active CN107123094B (en) | 2017-03-23 | 2017-03-23 | Video denoising method mixing Poisson, Gaussian and impulse noise |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107123094B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108499117A (en) * | 2018-04-11 | 2018-09-07 | 李�杰 | It can push type hanging basket swing |
| CN108596863A (en) * | 2018-06-11 | 2018-09-28 | 深圳市唯特视科技有限公司 | A kind of Poisson image de-noising method based on optimum linear prediction |
| CN108694706A (en) * | 2018-07-21 | 2018-10-23 | 罗杰敏 | A kind of video image denoising system |
| CN108955699A (en) * | 2018-07-21 | 2018-12-07 | 许晓山 | A kind of onboard navigation system |
| CN109903254A (en) * | 2019-03-04 | 2019-06-18 | 中国科学院长春光学精密机械与物理研究所 | Based on the improved bilateral filtering method of Poisson's kernel |
| CN111667428A (en) * | 2020-06-05 | 2020-09-15 | 北京百度网讯科技有限公司 | Noise generation method and device based on automatic search |
| CN112954136A (en) * | 2021-01-29 | 2021-06-11 | 中国科学院长春光学精密机械与物理研究所 | Method and device for suppressing shot noise of remote sensing image of aviation squint remote imaging |
| CN113139914A (en) * | 2021-03-16 | 2021-07-20 | 哈尔滨工业大学（深圳） | Image denoising method and device, electronic equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090324117A1 (en) * | 2008-06-27 | 2009-12-31 | Microsoft Corporation | Image denoising techniques |
| CN103136732A (en) * | 2013-02-19 | 2013-06-05 | 北京工业大学 | Image denosing method based on matrix filling |
| CN106157259A (en) * | 2016-06-24 | 2016-11-23 | 西安电子科技大学昆山创新研究院 | The video denoising method approached based on yardstick mixed model and low-rank |
| CN106331433A (en) * | 2016-08-25 | 2017-01-11 | 上海交通大学 | Video denoising method based on deep recursive neural network |
-
2017
- 2017-03-23 CN CN201710179189.9A patent/CN107123094B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090324117A1 (en) * | 2008-06-27 | 2009-12-31 | Microsoft Corporation | Image denoising techniques |
| CN103136732A (en) * | 2013-02-19 | 2013-06-05 | 北京工业大学 | Image denosing method based on matrix filling |
| CN106157259A (en) * | 2016-06-24 | 2016-11-23 | 西安电子科技大学昆山创新研究院 | The video denoising method approached based on yardstick mixed model and low-rank |
| CN106331433A (en) * | 2016-08-25 | 2017-01-11 | 上海交通大学 | Video denoising method based on deep recursive neural network |
Non-Patent Citations (3)
| Title |
|---|
| HUIJI，ET AL: "《Robust video denoising using low rank matrix completion》", 《COMPUTER VISION & PATTERN RECOGNITION》 * |
| 周宏潮，等: "《混合泊松高斯分布模型的参数估计》", 《中国空间科学技术》 * |
| 王圳萍，等: "《加权低秩矩阵恢复的混合噪声图像去噪》", 《计算机科学》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108499117A (en) * | 2018-04-11 | 2018-09-07 | 李�杰 | It can push type hanging basket swing |
| CN108499117B (en) * | 2018-04-11 | 2019-10-18 | 浦江县神力链条有限公司 | It can push type hanging basket swing |
| CN108596863A (en) * | 2018-06-11 | 2018-09-28 | 深圳市唯特视科技有限公司 | A kind of Poisson image de-noising method based on optimum linear prediction |
| CN108694706A (en) * | 2018-07-21 | 2018-10-23 | 罗杰敏 | A kind of video image denoising system |
| CN108955699A (en) * | 2018-07-21 | 2018-12-07 | 许晓山 | A kind of onboard navigation system |
| CN108955699B (en) * | 2018-07-21 | 2022-05-27 | 深圳市安则为科技有限公司 | Vehicle-mounted navigation system |
| CN109903254A (en) * | 2019-03-04 | 2019-06-18 | 中国科学院长春光学精密机械与物理研究所 | Based on the improved bilateral filtering method of Poisson's kernel |
| CN109903254B (en) * | 2019-03-04 | 2020-08-18 | 中国科学院长春光学精密机械与物理研究所 | Improved bilateral filtering method based on Poisson nucleus |
| CN111667428A (en) * | 2020-06-05 | 2020-09-15 | 北京百度网讯科技有限公司 | Noise generation method and device based on automatic search |
| CN112954136A (en) * | 2021-01-29 | 2021-06-11 | 中国科学院长春光学精密机械与物理研究所 | Method and device for suppressing shot noise of remote sensing image of aviation squint remote imaging |
| CN113139914A (en) * | 2021-03-16 | 2021-07-20 | 哈尔滨工业大学（深圳） | Image denoising method and device, electronic equipment and storage medium |
| CN113139914B (en) * | 2021-03-16 | 2023-02-17 | 哈尔滨工业大学（深圳） | Image denoising method and device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107123094B (en) | 2020-07-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107123094A (en) | A kind of mixing Poisson, the video denoising method of gaussian sum impulsive noise | |
| CN110119780B (en) | Hyper-spectral image super-resolution reconstruction method based on generation countermeasure network | |
| CN106683067B (en) | Deep learning super-resolution reconstruction method based on residual sub-images | |
| US20220044375A1 (en) | Saliency Map Enhancement-Based Infrared and Visible Light Fusion Method | |
| CN112767251B (en) | Image super-resolution method based on multi-scale detail feature fusion neural network | |
| Zhang et al. | Image compressive sensing recovery via collaborative sparsity | |
| CN105976318A (en) | Image super-resolution reconstruction method | |
| CN109523470A (en) | A kind of depth image super resolution ratio reconstruction method and system | |
| CN105046672A (en) | Method for image super-resolution reconstruction | |
| CN106485671A (en) | Multi-direction Weighted T V based on edge and self-similarity constraint image deblurring method | |
| CN107680116A (en) | A kind of method for monitoring moving object in video sequences | |
| CN103595981A (en) | Method for demosaicing color filtering array image based on non-local low rank | |
| CN105488759B (en) | A kind of image super-resolution rebuilding method based on local regression model | |
| CN104200441B (en) | Higher-order singular value decomposition based magnetic resonance image denoising method | |
| CN112634163A (en) | Method for removing image motion blur based on improved cycle generation countermeasure network | |
| CN111882485B (en) | Hierarchical feature feedback fusion depth image super-resolution reconstruction method | |
| Zheng et al. | Single-image deraining via recurrent residual multiscale networks | |
| CN116152591B (en) | Model training method, infrared small target detection method and device and electronic equipment | |
| CN105427243A (en) | Video super-resolution reconstruction method based on adaptive interpolation kernel learning | |
| CN104036468A (en) | Super-resolution reconstruction method for single-frame images on basis of pre-amplification non-negative neighbor embedding | |
| CN111882516B (en) | Image quality evaluation method based on visual saliency and deep neural network | |
| CN110415173A (en) | A kind of image super-resolution method based on three-dimensional total variation | |
| CN116563916A (en) | Attention fusion-based cyclic face super-resolution method and system | |
| CN102222321A (en) | Blind reconstruction method for video sequence | |
| Nugroho et al. | Improvement Of Image Quality Using Convolutional Neural Networks Method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20230419 Address after: 519000 room 501b, building 6, No. 19, Yongtian Road, Xiangzhou District, Zhuhai City, Guangdong Province Patentee after: Guangdong Boke Electronic Technology Co.,Ltd. Address before: Room 302, Zone B, Building 1, Innovation Center, Science and Technology Innovation Zone, Mianyang City, Sichuan Province, 621000 Patentee before: SICHUAN JINGMU TECHNOLOGY CO.,LTD. |
|
| PE01 | Entry into force of the registration of the contract for pledge of patent right | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: A video denoising method using a mixture of Poisson, Gaussian, and impulse noise Effective date of registration: 20231018 Granted publication date: 20200717 Pledgee: Agricultural Bank of China Limited Zhongshan Tanzhou sub branch Pledgor: Guangdong Boke Electronic Technology Co.,Ltd. Registration number: Y2023980061590 |