5.3.4.2 Patch-Based Restoration, Patch Based Denoising

Chapter Contents (Back)
Patch-Based. Region-Based. Adaptive. Restoration. Noise.

Chen, Q.A.[Qi-Ang], Sun, Q.S.[Quan-Sen], Xia, D.S.[De-Shen],
Homogeneity similarity based image denoising,
PR(43), No. 12, December 2010, pp. 4089-4100.
Elsevier DOI 1003
Image denoising; Homogeneity similarity; Patch-based method; Structure similarity BibRef

Yang, S.Y.[Shu-Yuan], Zhao, L.F.[Lin-Fang], Wang, M.[Min], Zhang, Y.Y.[Yue-Yuan], Jiao, L.C.[Li-Cheng],
Dictionary learning and similarity regularization based image noise reduction,
JVCIR(24), No. 2, February 2013, pp. 181-186.
Elsevier DOI 1302
Image denoising; Sparse learning; Image patches; Dictionary learning; Self-similarity; Non-local means; KSVD; Non-parametric Bayesian BibRef

Delon, J.[Julie], Desolneux, A.[Agnès],
A Patch-Based Approach for Removing Impulse or Mixed Gaussian-Impulse Noise,
SIIMS(6), No. 2, 2013, pp. 1140-1174.
DOI Link 1307
BibRef

Delon, J.[Julie], Desolneux, A.[Agnès], Guillemot, T.[Thierry],
PARIGI: a Patch-based Approach to Remove Impulse-Gaussian Noise from Images,
IPOL(6), 2016, pp. 130-154.
DOI Link 1608
Code, Impulse Noise. BibRef

Wang, R.X.[Rui-Xuang], Pakleppa, M., Trucco, E.[Emanuele],
Low-Rank Prior in Single Patches for Nonpointwise Impulse Noise Removal,
IP(24), No. 5, May 2015, pp. 1485-1496.
IEEE DOI 1504
gradient methods BibRef

Wang, R.X.[Rui-Xuang], Trucco, E.[Emanuele],
Single-Patch Low-Rank Prior for Non-pointwise Impulse Noise Removal,
ICCV13(1073-1080)
IEEE DOI 1403
joint low-rank and sparse matrix recovery framework BibRef

Chen, X.G.[Xiao-Gang], Kang, S.B.[Sing Bing], Yang, J.[Jie], Yu, J.Y.[Jing-Yi],
Fast Edge-Aware Denoising by Approximated Patch Geodesic Paths,
CirSysVideo(25), No. 6, June 2015, pp. 897-909.
IEEE DOI 1506
BibRef
Earlier:
Fast Patch-Based Denoising Using Approximated Patch Geodesic Paths,
CVPR13(1211-1218)
IEEE DOI 1309
Acceleration BibRef

Feng, J.Z.[Jian-Zhou], Song, L.[Li], Huo, X.M.[Xiao-Ming], Yang, X.K.[Xiao-Kang], Zhang, W.J.[Wen-Jun],
An Optimized Pixel-Wise Weighting Approach for Patch-Based Image Denoising,
SPLetters(22), No. 1, January 2015, pp. 115-119.
IEEE DOI 1410
BibRef
Earlier:
New bounds on image denoising: Viewpoint of sparse representation and non-local averaging,
VCIP12(1-5).
IEEE DOI 1302
filtering theory BibRef

Liu, G.C.[Gan-Chao], Zhong, H.[Hua], Jiao, L.C.[Li-Cheng],
Comparing Noisy Patches for Image Denoising: A Double Noise Similarity Model,
IP(24), No. 3, March 2015, pp. 862-872.
IEEE DOI 1502
Gaussian noise BibRef

Niknejad, M., Rabbani, H., Babaie-Zadeh, M.,
Image Restoration Using Gaussian Mixture Models With Spatially Constrained Patch Clustering,
IP(24), No. 11, November 2015, pp. 3624-3636.
IEEE DOI 1509
Gaussian distribution BibRef

Papyan, V., Elad, M.,
Multi-Scale Patch-Based Image Restoration,
IP(25), No. 1, January 2016, pp. 249-261.
IEEE DOI 1601
Approximation methods BibRef

Cai, N.[Nian], Zhou, Y.[Yang], Wang, S.R.[Sheng-Ru], Ling, B.W.K.[Bingo Wing-Kuen], Weng, S.W.[Shao-Wei],
Image denoising via patch-based adaptive Gaussian mixture prior method,
SIViP(10), No. 6, June 2016, pp. 993-999.
Springer DOI 1608
BibRef

Romano, Y.[Yaniv], Elad, M.[Michael],
Con-Patch: When a Patch Meets Its Context,
IP(25), No. 9, September 2016, pp. 3967-3978.
IEEE DOI 1609
image denoising BibRef

Dai, T.[Tao], Xu, Z.Y.[Zhi-Ya], Liang, H.[Haoyi], Gu, K.[Ke], Tang, Q.T.[Qing-Tao], Wang, Y.S.[Yi-Sen], Lu, W.Z.[Wei-Zhi], Xia, S.T.[Shu-Tao],
A generic denoising framework via guided principal component analysis,
JVCIR(48), No. 1, 2017, pp. 340-352.
Elsevier DOI 1708
Image denoising BibRef

Dai, T.[Tao], Gu, K.[Ke], Tang, Q.T.[Qing-Tao], Hung, K.W., Zhang, Y.B., Lu, W.Z.[Wei-Zhi], Xia, S.T.[Shu-Tao],
Foveated nonlocal dual denoising,
ICIP17(1881-1885)
IEEE DOI 1803
Discrete Fourier transforms, Entropy, Frequency-domain analysis, Image denoising, Noise measurement, Noise reduction, Image denoising BibRef

Chen, Y., Dai, T., Xiao, X., Lu, J., Xia, S.T.,
Enhanced Image Restoration Via Supervised Target Feature Transfer,
ICIP20(1028-1032)
IEEE DOI 2011
Feature extraction, Image restoration, Training, Image reconstruction, Task analysis, Image resolution, Autoencoder BibRef

Dai, T.[Tao], Song, C.B.[Chao-Bing], Zhang, J.P.[Ji-Ping], Xia, S.T.[Shu-Tao],
PMPA: A patch-based multiscale products algorithm for image denoising,
ICIP15(4406-4410)
IEEE DOI 1512
Image denoising; LAWML; multiscale products; nonlocal means; wavelet BibRef

Wang, S., Ding, Z., Fu, Y.,
Marginalized Denoising Dictionary Learning With Locality Constraint,
IP(27), No. 1, January 2018, pp. 500-510.
IEEE DOI 1712
Dictionaries, Encoding, Feature extraction, Machine learning, Noise measurement, Noise reduction, Training, locality constraint BibRef

Yao, S.[Shoukui], Chang, Y.[Yi], Qin, X.J.[Xiao-Juan], Zhang, Y.Z.[Yao-Zong], Zhang, T.X.[Tian-Xu],
Principal component dictionary-based patch grouping for image denoising,
JVCIR(50), No. 1, 2018, pp. 111-122.
Elsevier DOI 1712
Nonlocal self-similarity BibRef

Hu, H.J.[Hai-Juan], Froment, J.[Jacques], Liu, Q.S.[Quan-Sheng],
A note on patch-based low-rank minimization for fast image denoising,
JVCIR(50), No. 1, 2018, pp. 100-110.
Elsevier DOI 1712
Image denoising BibRef

Xu, J.[Jun], Zhang, L.[Lei], Zhang, D.[David],
External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising,
IP(27), No. 6, June 2018, pp. 2996-3010.
IEEE DOI 1804
AWGN, Cameras, Dictionaries, Image denoising, Learning systems, Noise measurement, Noise reduction, Image denoising, real-world noisy image BibRef

Xu, J.[Jun], Ren, D.W.[Dong-Wei], Zhang, L.[Lei], Zhang, D.[David],
Patch Group Based Bayesian Learning for Blind Image Denoising,
NTIRE16(I: 79-95).
Springer DOI 1704
BibRef

Xu, J.[Jun], Zhang, L.[Lei], Zuo, W., Zhang, D.[David], Feng, X.,
Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising,
ICCV15(244-252)
IEEE DOI 1602
Dictionaries BibRef

Gu, S.H.[Shu-Hang], Xie, Q.[Qi], Meng, D.Y.[De-Yu], Zuo, W.M.[Wang-Meng], Feng, X.C.[Xiang-Chu], Zhang, L.[Lei],
Weighted Nuclear Norm Minimization and Its Applications to Low Level Vision,
IJCV(121), No. 2, January 2017, pp. 183-208.
Springer DOI 1702
BibRef
Earlier: A1, A6, A4, A5, Only:
Weighted Nuclear Norm Minimization with Application to Image Denoising,
CVPR14(2862-2869)
IEEE DOI 1409
BibRef

Xu, J.[Jun], Zhang, L.[Lei], Zhang, D.[David], Feng, X.C.[Xiang-Chu],
Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising,
ICCV17(1105-1113)
IEEE DOI 1802
image colour analysis, image denoising, minimisation, statistics, MC-WNNM model, channel redundancy, color image denoising, Noise reduction BibRef

Chen, F., Zhang, L., Yu, H.,
External Patch Prior Guided Internal Clustering for Image Denoising,
ICCV15(603-611)
IEEE DOI 1602
Covariance matrices BibRef

Zhang, M., Desrosiers, C.,
High-quality Image Restoration Using Low-Rank Patch Regularization and Global Structure Sparsity,
IP(28), No. 2, February 2019, pp. 868-879.
IEEE DOI 1811
Image reconstruction, Image restoration, Adaptation models, Task analysis, Image resolution, Noise reduction, Image coding, ADMM BibRef

Parameswaran, S., Deledalle, C., Denis, L., Nguyen, T.Q.,
Accelerating GMM-Based Patch Priors for Image Restoration: Three Ingredients for a 100X Speed-Up,
IP(28), No. 2, February 2019, pp. 687-698.
IEEE DOI 1811
approximation theory, expectation-maximisation algorithm, Gaussian processes, image denoising, image resolution, efficient algorithms BibRef

Liu, H., Xiong, R., Liu, D., Ma, S., Wu, F., Gao, W.,
Image Denoising via Low Rank Regularization Exploiting Intra and Inter Patch Correlation,
CirSysVideo(28), No. 12, December 2018, pp. 3321-3332.
IEEE DOI 1812
BibRef
Earlier: A1, A2, A3, A5, A6, Only:
Low rank regularization exploiting intra and inter patch correlation for image denoising,
VCIP17(1-4)
IEEE DOI 1804
Correlation, Principal component analysis, Uncertainty, Image denoising, Optimization, Transforms, Noise reduction, inter-patch correlation. Bayes methods, image restoration, iterative methods, minimisation, PSNR, non-local similarity BibRef

Deledalle, C., Parameswaran, S., Nguyen, T.,
Image Denoising with Generalized Gaussian Mixture Model Patch Priors,
SIIMS(11), No. 4, 2018, pp. 2568-2609.
DOI Link 1901
BibRef

Hurault, S.[Samuel], Ehret, T.[Thibaud], Arias, P.[Pablo],
EPLL: An Image Denoising Method Using a Gaussian Mixture Model Learned on a Large Set of Patches,
IPOL(8), 2018, pp. 465-489.
DOI Link 1901
Code, Noise Removal.
See also From learning models of natural image patches to whole image restoration. BibRef

Niknejad, M., Bioucas-Dias, J., Figueiredo, M.A.T.,
External Patch-Based Image Restoration Using Importance Sampling,
IP(28), No. 9, Sep. 2019, pp. 4460-4470.
IEEE DOI 1908
BibRef
Earlier:
Class-specific poisson denoising by patch-based importance sampling,
ICIP17(1247-1251)
IEEE DOI 1803
BibRef
And:
Class-specific image denoising using importance sampling,
ICIP17(1242-1246)
IEEE DOI 1803
estimation theory, image restoration, image sampling, importance sampling, least mean squares methods. Clustering algorithms, Face, Gaussian distribution, Monte Carlo methods, Noise measurement, Noise reduction. Bayes methods, Image denoising, non-local means BibRef

Hong, I.[Inpyo], Hwang, Y.[Youngbae], Kim, D.[Daeyoung],
Efficient deep learning of image denoising using patch complexity local divide and deep conquer,
PR(96), 2019, pp. 106945.
Elsevier DOI 1909
Image denoising, Deep neural networks, Stacked denoising autoencoders, Divide and conquer, Image patch classification BibRef

Routray, S.[Sidheswar], Ray, A.K.[Arun Kumar], Mishra, C.[Chandrabhanu],
An efficient image denoising method based on principal component analysis with learned patch groups,
SIViP(13), No. 7, October 2019, pp. 1405-1412.
Springer DOI 1911
BibRef

Colak, O.[Ozden], Eksioglu, E.M.[Ender M.],
Image denoising using patch ordering and 3D transformation of patches,
IET-IPR(13), No. 13, November 2019, pp. 2636-2646.
DOI Link 1911
BibRef

Ji, J.[Jian], Wei, J.J.[Jia-Jie], Fan, G.L.[Guo-Liang], Bai, M.Q.[Meng-Qi], Huang, J.J.[Jing-Jing], Miao, Q.G.[Qi-Guang],
Image patch prior learning based on random neighbourhood resampling for image denoising,
IET-IPR(14), No. 5, 17 April 2020, pp. 838-844.
DOI Link 2004
BibRef

Zha, Z.Y.[Zhi-Yuan], Yuan, X.[Xin], Wen, B.H.[Bi-Han], Zhang, J.C.[Jia-Chao], Zhou, J.T.[Jian-Tao], Zhu, C.[Ce],
Image Restoration Using Joint Patch-Group-Based Sparse Representation,
IP(29), 2020, pp. 7735-7750.
IEEE DOI 2007
Sparse representation, JPG-SR, nonlocal self-similarity, image restoration, ADMM BibRef

Yue, H., Liu, J., Yang, J., Sun, X., Nguyen, T.Q., Wu, F.,
IENet: Internal and External Patch Matching ConvNet for Web Image Guided Denoising,
CirSysVideo(30), No. 11, November 2020, pp. 3928-3942.
IEEE DOI 2011
Noise reduction, Image denoising, Correlation, Noise measurement, Gain, Neural networks, Image resolution, Image denoising, internal and external patch matching ConvNet (IENet) BibRef

Fu, Y.L.[Yu-Li], Xu, J.W.[Jun-Wei], Xiang, Y.J.[You-Jun], Chen, Z.[Zhen], Zhu, T.[Tao], Cai, L.[Lei], He, W.H.[Wei-Hong],
Multi-scale patches based image denoising using weighted nuclear norm minimisation,
IET-IPR(14), No. 13, November 2020, pp. 3161-3168.
DOI Link 2012
BibRef

Shi, M.W.[Miao-Wen], Zhang, F.[Fan], Wang, S.W.[Su-Wei], Zhang, C.M.[Cai-Ming], Li, X.M.[Xue-Mei],
Detail preserving image denoising with patch-based structure similarity via sparse representation and SVD,
CVIU(206), 2021, pp. 103173.
Elsevier DOI 2104
Structure similarity, Sparse representation, Singular value decomposition( SVD), Image denoising BibRef

Yao, D.[Dan], McLaughlin, S.[Stephen], Altmann, Y.[Yoann],
Patch-Based Image Restoration Using Expectation Propagation,
SIIMS(15), No. 1, 2022, pp. 192-227.
DOI Link 2204
BibRef

Yao, D.[Dan], Altmann, Y.[Yoann], McLaughlin, S.[Stephen],
Color Image Restoration in the Low Photon-Count Regime Using Expectation Propagation,
ICIP22(3126-3130)
IEEE DOI 2211
Couplings, Uncertainty, Image color analysis, Gaussian noise, Color, Gray-scale, Image restoration, color image restoration, Expectation Propagation BibRef

Yao, D.[Dan], McLaughlin, S.[Stephen], Altmann, Y.[Yoann],
Fast Scalable Image Restoration Using Total Variation Priors and Expectation Propagation,
IP(31), 2022, pp. 5762-5773.
IEEE DOI 2209
Image restoration, Bayes methods, TV, Uncertainty, Image edge detection, Estimation, Noise reduction, hyperparameter estimation BibRef

Shi, H.[Hui], Traonmilin, Y.[Yann], Aujol, J.F.[Jean-Francois],
Compressive Learning for Patch-Based Image Denoising,
SIIMS(15), No. 3, 2022, pp. 1184-1212.
DOI Link 2208
BibRef

Kim, J.[Junseob], Cho, S.[Sunghwan], Hwang, S.I.[Sun-Il], Lee, W.[Wonjin], Choi, Y.[Yeongyoon],
Enhancing LPI Radar Signal Classification Through Patch-Based Noise Reduction,
SPLetters(31), 2024, pp. 716-720.
IEEE DOI 2403
Noise reduction, Radar, Convolution, Noise measurement, Signal to noise ratio, Image reconstruction, signal classification BibRef

Cho, H.[Hyunjun], Shin, H.K.[Hong-Kyu], Jang, Y.[Yurim], Ko, S.J.[Sung-Jea], Jung, S.W.[Seung-Won],
PD-CR: Patch-Based Diffusion Using Constrained Refinement for Image Restoration,
SPLetters(31), 2024, pp. 949-953.
IEEE DOI 2404
Image restoration, Computational modeling, Task analysis, Vectors, Probabilistic logic, Noise reduction, raindrop removal BibRef

Herbreteau, S.[Sébastien], Kervrann, C.[Charles],
Linear Combinations of Patches are Unreasonably Effective for Single-Image Denoising,
IP(33), 2024, pp. 4600-4613.
IEEE DOI 2408
Noise reduction, Noise measurement, Training, Image denoising, Transforms, Optimization, Inverse problems, statistical aggregation BibRef


Liang, Y.W.[Yi-Wen], Wang, L.[Lu], Wang, J.F.[Jian-Fei], Luo, Y.[Ye],
Attentive Deep K-SVD Network for Patch Correlated Image Denoising,
ICIP23(1490-1494)
IEEE DOI 2312
BibRef

Wang, Z.C.[Zi-Chun], Fu, Y.[Ying], Liu, J.[Ji], Zhang, Y.[Yulun],
LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoising,
CVPR23(18156-18165)
IEEE DOI 2309
BibRef

Dutta, S.[Sayantan], Basarab, A.[Adrian], Georgeot, B.[Bertrand], Kouamé, D.[Denis],
Deep Unfolding of Image Denoising by Quantum Interactive Patches,
ICIP22(491-495)
IEEE DOI 2211
Adaptation models, Adaptive systems, Noise reduction, Superresolution, Stacking, Quantum mechanics, Skeleton, quantum image processing BibRef

Bodduna, K., Weickert, J.,
Enhancing Patch-Based Methods with Inter-Frame Connectivity for Denoising Multi-Frame Images,
ICIP19(2414-2418)
IEEE DOI 1910
Multi-frame denoising, non-local patch methods, additive white Gaussian noise BibRef

Lu, S.,
Good Similar Patches for Image Denoising,
WACV19(1886-1895)
IEEE DOI 1904
Gaussian processes, image denoising, mixture models, pattern clustering, input image, Image restoration BibRef

Kwak, N.[Nojun], Yoo, J.[Jaeyoung], Lee, S.H.[Sang-Ho],
Image Restoration by Estimating Frequency Distribution of Local Patches,
CVPR18(6684-6692)
IEEE DOI 1812
Image restoration, Image coding, Frequency-domain analysis, Feature extraction, Discrete cosine transforms BibRef

Ren, Z.H.[Zhi-Hang], Dai, P.[Peng], Liu, S.C.[Shuai-Cheng], Zhu, S.Y.[Shu-Yuan], Zeng, B.[Bing],
Coding Trajectory: Enable Video Coding for Video Denoising,
ICIP18(3224-3228)
IEEE DOI 1809
Use redundant patches in video frames. Trajectory, Video coding, Noise reduction, Image coding, Encoding, Discrete cosine transforms, Video Coding, video denoising, coding trajectory BibRef

Arias, P., Facciolo, G.[Gabriele], Morel, J.M.[Jean-Michel],
A Comparison of Patch-Based Models in Video Denoising,
IVMSP18(1-5)
IEEE DOI 1809
Discrete cosine transforms, Adaptation models, Noise reduction, Covariance matrices, patch-based methods BibRef

Wang, Y.[Yan], Cho, S.H.[Sung-Hyun], Wang, J.[Jue], Chang, S.F.[Shih-Fu],
Discriminative Indexing for Probabilistic Image Patch Priors,
ECCV14(IV: 200-214).
Springer DOI 1408
;E.g. Expected Patch Log-Likelihood (EPLL).
See also From learning models of natural image patches to whole image restoration. BibRef

de Smet, V., Namboodiri, V.P., Van Gool, L.J.,
Nonuniform image patch exemplars for low level vision,
WACV13(23-30).
IEEE DOI 1303
for denoising and super-resolution. BibRef

Pierazzo, N.[Nicola], Rais, M.[Martin],
Boosting 'shotgun denoising' by patch normalization,
ICIP13(1115-1119)
IEEE DOI 1402
Approximation methods. Patch based. BibRef

Zontak, M.[Maria], Mosseri, I.[Inbar], Irani, M.[Michal],
Separating Signal from Noise Using Patch Recurrence across Scales,
CVPR13(1195-1202)
IEEE DOI 1309
image denoising; multi-scale prior for noisy images; patch recurrence BibRef

Jia, C.[Chao], Evans, B.L.[Brian L.],
Patch-based image deconvolution via joint modeling of sparse priors,
ICIP11(681-684).
IEEE DOI 1201
BibRef

Do, Q.B.[Quoc Bao], Beghdadi, A.[Azeddine], Luong, M.[Marie],
Image denoising using Bilateral filter in high dimensional patch-space,
EUVIP11(36-41).
IEEE DOI 1110
BibRef
Earlier:
Image Denoising Using Bilateral Filter in High Dimensional PCA-Space,
CAIP11(II: 372-379).
Springer DOI 1109
BibRef

Angelino, C.V.[Cesario Vincenzo], Debreuve, E.[Eric], Barlaud, M.[Michel],
Patch confidence k-nearest neighbors denoising,
ICIP10(1129-1132).
IEEE DOI 1009
BibRef
And:
Image restoration using a kNN-variant of the mean-shift,
ICIP08(573-576).
IEEE DOI 0810
BibRef

Salmon, J., Le Pennec, E.,
NL-Means and aggregation procedures,
ICIP09(2977-2980).
IEEE DOI 0911
Patch based denoising. BibRef

Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Iterative, Recursive, Restoration Techniques .


Last update:Nov 26, 2024 at 16:40:19