5.3.13.1 Hyperspectral Images Restoration, Denoising

Zhang, Y., Duijster, A., Scheunders, P.,
A Bayesian Restoration Approach for Hyperspectral Images,
GeoRS(50), No. 9, September 2012, pp. 3453-3462.
IEEE DOI 1209
BibRef

Guo, X.[Xian], Huang, X.[Xin], Zhang, L.P.[Liang-Pei], Zhang, L.F.[Le-Fei],
Hyperspectral Image Noise Reduction Based on Rank-1 Tensor Decomposition,
PandRS(83), No. 1, 2013, pp. 50-63.
Elsevier DOI 1308
Tensor decomposition
See also modified stochastic neighbor embedding for multi-feature dimension reduction of remote sensing images, A. BibRef

Zhang, H.Y.[Hong-Yan], He, W.[Wei], Zhang, L.P.[Liang-Pei], Shen, H.F.[Huan-Feng], Yuan, Q.,
Hyperspectral Image Restoration Using Low-Rank Matrix Recovery,
GeoRS(52), No. 8, August 2014, pp. 4729-4743.
IEEE DOI 1403
Gaussian noise BibRef

He, W.[Wei], Zhang, H.Y.[Hong-Yan], Zhang, L.P.[Liang-Pei], Shen, H.F.[Huan-Feng],
Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration,
GeoRS(54), No. 1, January 2016, pp. 178-188.
IEEE DOI 1601
hyperspectral imaging BibRef

Zheng, Y.B.[Yu-Bang], Huang, T.Z.[Ting-Zhu], Zhao, X.L.[Xi-Le], Chen, Y.[Yong], He, W.[Wei],
Double-Factor-Regularized Low-Rank Tensor Factorization for Mixed Noise Removal in Hyperspectral Image,
GeoRS(58), No. 12, December 2020, pp. 8450-8464.
IEEE DOI 2012
Image restoration, Tensors, Gray-scale, Principal component analysis, Matrix decomposition, proximal alternating minimization (PAM)
See also Weighted Low-Rank Tensor Recovery for Hyperspectral Image Restoration. BibRef

He, W.[Wei], Zhang, H.Y.[Hong-Yan], Zhang, L.P.[Liang-Pei],
Total Variation Regularized Reweighted Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing,
GeoRS(55), No. 7, July 2017, pp. 3909-3921.
IEEE DOI 1706
Adaptation models, Hyperspectral imaging, Minimization, Robustness, Sparse matrices, TV, Blind unmixing, hyperspectral image, nonnegative matrix factorization (NMF), reweighted sparsity, total, variation, (TV) BibRef

He, W.[Wei], Yokoya, N.[Naoto], Yuan, L.H.[Long-Hao], Zhao, Q.B.[Qi-Bin],
Remote Sensing Image Reconstruction Using Tensor Ring Completion and Total Variation,
GeoRS(57), No. 11, November 2019, pp. 8998-9009.
IEEE DOI 1911
Image reconstruction, Matrix decomposition, TV, Remote sensing, Cloud computing, MODIS, Cloud removal, gap filling, reconstruction, total variation (TV) BibRef

Zhang, H.Y.[Hong-Yan], Liu, L.[Lu], He, W.[Wei], Zhang, L.P.[Liang-Pei],
Hyperspectral Image Denoising With Total Variation Regularization and Nonlocal Low-Rank Tensor Decomposition,
GeoRS(58), No. 5, May 2020, pp. 3071-3084.
IEEE DOI 2005
Noise reduction, TV, Hyperspectral imaging, Image restoration, Gaussian noise, Denoising, Hyperspectral image (HSI), tensor decomposition BibRef

Chen, Y.[Yong], Huang, T.Z.[Ting-Zhu], He, W.[Wei], Yokoya, N.[Naoto], Zhao, X.L.[Xi-Le],
Hyperspectral Image Compressive Sensing Reconstruction Using Subspace-Based Nonlocal Tensor Ring Decomposition,
IP(29), 2020, pp. 6813-6828.
IEEE DOI 2007
Tensile stress, Correlation, Image coding, Image reconstruction, TV, Computational efficiency, Dictionaries, Compressive sensing, tensor ring decomposition BibRef

Lin, J.[Jie], Huang, T.Z.[Ting-Zhu], Zhao, X.L.[Xi-Le], Jiang, T.X.[Tai-Xiang], Zhuang, L.[Lina],
A Tensor Subspace Representation-Based Method for Hyperspectral Image Denoising,
GeoRS(59), No. 9, September 2021, pp. 7739-7757.
IEEE DOI 2109
Tensors, Gaussian noise, Computational modeling, Noise reduction, Minimization, Computational complexity, Hyperspectral imaging, tensor subspace representation (TenSR) BibRef

Chen, Y.[Yong], He, W.[Wei], Yokoya, N.[Naoto], Huang, T.Z.[Ting-Zhu], Zhao, X.L.[Xi-Le],
Nonlocal Tensor-Ring Decomposition for Hyperspectral Image Denoising,
GeoRS(58), No. 2, February 2020, pp. 1348-1362.
IEEE DOI 2001
Noise reduction, Correlation, Matrix decomposition, Hyperspectral imaging, Data models, Denoising, tensor-ring (TR) decomposition BibRef

Rasti, B., Sveinsson, J.R., Ulfarsson, M.O.,
Wavelet-Based Sparse Reduced-Rank Regression for Hyperspectral Image Restoration,
GeoRS(52), No. 10, October 2014, pp. 6688-6698.
IEEE DOI 1407
Hyperspectral imaging BibRef

Zhao, Y.Q.[Yong-Qiang], Yang, J.X.[Jing-Xiang],
Hyperspectral Image Denoising via Sparse Representation and Low-Rank Constraint,
GeoRS(53), No. 1, January 2015, pp. 296-308.
IEEE DOI 1410
approximation theory BibRef

Yang, J.X.[Jing-Xiang], Zhao, Y.Q.[Yong-Qiang], Chan, J.C.W., Kong, S.G.,
Coupled Sparse Denoising and Unmixing With Low-Rank Constraint for Hyperspectral Image,
GeoRS(54), No. 3, March 2016, pp. 1818-1833.
IEEE DOI 1603
Dictionaries
See also Learning and Transferring Deep Joint Spectral-Spatial Features for Hyperspectral Classification. BibRef

Xue, J.[Jize], Zhao, Y.Q.[Yong-Qiang], Liao, W.Z.[Wen-Zhi], Kong, S.G.,
Joint Spatial and Spectral Low-Rank Regularization for Hyperspectral Image Denoising,
GeoRS(56), No. 4, April 2018, pp. 1940-1958.
IEEE DOI 1804
Correlation, Dictionaries, Gaussian noise, Hyperspectral imaging, Noise reduction, Spectral analysis, spectrum correlation BibRef

Kong, X.Y.[Xiang-Yang], Zhao, Y.Q.[Yong-Qiang], Xue, J.[Jize], Chan, J.C.W.[Jonathan Cheung-Wai], Ren, Z.G.[Zhi-Gang], Huang, H.X.[Hai-Xia], Zang, J.Y.[Ji-Yuan],
Hyperspectral Image Denoising Based on Nonlocal Low-Rank and TV Regularization,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Kong, X.Y.[Xiang-Yang], Zhao, Y.Q.[Yong-Qiang], Xue, J.[Jize], Chan, J.C.W.[Jonathan Cheung-Wai],
Hyperspectral Image Denoising Using Global Weighted Tensor Norm Minimum and Nonlocal Low-Rank Approximation,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Xue, J.[Jize], Zhao, Y.Q.[Yong-Qiang], Liao, W.Z.[Wen-Zhi], Chan, J.C.W.[Jonathan Cheung-Wai],
Nonlocal Tensor Sparse Representation and Low-Rank Regularization for Hyperspectral Image Compressive Sensing Reconstruction,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Xue, J.[Jize], Zhao, Y.Q.[Yong-Qiang], Bu, Y., Liao, W.Z.[Wen-Zhi], Chan, J.C.W.[Jonathan Cheung-Wai], Philips, W.,
Spatial-Spectral Structured Sparse Low-Rank Representation for Hyperspectral Image Super-Resolution,
IP(30), 2021, pp. 3084-3097.
IEEE DOI 1806
Superresolution, Sparse matrices, Spatial resolution, Dictionaries, Correlation, Tensors, Task analysis, affinity matrix BibRef

Xue, J.[Jize], Zhao, Y.Q.[Yong-Qiang], Liao, W.Z.[Wen-Zhi], Chan, J.C.,
Nonlocal Low-Rank Regularized Tensor Decomposition for Hyperspectral Image Denoising,
GeoRS(57), No. 7, July 2019, pp. 5174-5189.
IEEE DOI 1907
Noise reduction, Estimation, Correlation, TV, Hyperspectral imaging, CANDECOMP/PARAFAC (CP) tensor decomposition (CPTD), rank estimation bias BibRef

Xue, J., Zhao, Y.,
Rank-1 Tensor Decomposition for Hyperspectral Image Denoising with Nonlocal Low-Rank Regularization,
CMVIT17(40-45)
IEEE DOI 1704
hyperspectral imaging BibRef

Zhao, Y.Q.[Yong-Qiang], Xue, J.[Jize], Hao, J.,
Tensor non-local low-rank regularization for recovering compressed hyperspectral images,
ICIP17(3046-3050)
IEEE DOI 1803
Correlation, Hyperspectral imaging, Image coding, Image reconstruction, Matrix decomposition, Tensile stress, tensor low-rank approximation BibRef

Yi, C., Zhao, Y.Q.[Yong-Qiang], Yang, J.X.[Jing-Xiang], Chan, J.C.W., Kong, S.G.,
Joint Hyperspectral Superresolution and Unmixing With Interactive Feedback,
GeoRS(55), No. 7, July 2017, pp. 3823-3834.
IEEE DOI 1706
Degradation, Distortion, Hyperspectral imaging, Image reconstruction, Spatial resolution, Hyperspectral image (HSI), interactive feedback, sparsity, spectral unmixing, superresolution, enhancement BibRef

Karami, A., Heylen, R., Scheunders, P.,
Band-Specific Shearlet-Based Hyperspectral Image Noise Reduction,
GeoRS(53), No. 9, September 2015, pp. 5054-5066.
IEEE DOI 1506
Correlation BibRef

Karami, A.[Azam], Heylen, R.[Rob], Scheunders, P.[Paul],
Hyperspectral Image Compression Optimized for Spectral Unmixing,
GeoRS(54), No. 10, October 2016, pp. 5884-5894.
IEEE DOI 1610
data compression BibRef

Ghasrodashti, E.K.[Elham Kordi], Karami, A.[Azam], Heylen, R.[Rob], Scheunders, P.[Paul],
Spatial Resolution Enhancement of Hyperspectral Images Using Spectral Unmixing and Bayesian Sparse Representation,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Huo, L.G.[Lei-Gang], Feng, X.C.[Xiang-Chu], Huo, C.L.[Chun-Lei], Pan, C.H.[Chun-Hong],
Learning Deep Dictionary for Hyperspectral Image Denoising,
IEICE(E98-D), No. 7, July 2015, pp. 1401-1404.
WWW Link. 1508
BibRef

Li, C.[Chang], Ma, Y.[Yong], Huang, J.[Jun], Mei, X.G.[Xiao-Guang], Ma, J.Y.[Jia-Yi],
Hyperspectral Image Denoising Using the Robust Low-Rank Tensor Recovery,
JOSA-A(32), No. 9, September 2015, pp. 1604-1612.
DOI Link 1509
Spectroscopy, visible; Multispectral and hyperspectral imaging
See also MsLRR: A Unified Multiscale Low-Rank Representation for Image Segmentation. BibRef

Fan, H.Y.[Hai-Yan], Li, C.[Chang], Guo, Y.L.[Yu-Lan], Kuang, G.Y.[Gang-Yao], Ma, J.Y.[Jia-Yi],
Spatial-Spectral Total Variation Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising,
GeoRS(56), No. 10, October 2018, pp. 6196-6213.
IEEE DOI 1810
Tensile stress, TV, Hyperspectral imaging, Noise reduction, Manganese, Gaussian noise, Hyperspectral image (HSI) denoising, spatial-spectral total variation (SSTV) BibRef

Ma, J.Y.[Jia-Yi], Jiang, J., Li, C.[Chang],
Hyperspectral Image Denoising with Segmentation-Based Low Rank Representation,
VCIP16(1-4)
IEEE DOI 1701
Gaussian noise BibRef

Ma, J.Y.[Jia-Yi], Li, C.[Chang], Ma, Y.[Yong], Wang, Z.,
Hyperspectral Image Denoising Based on Low-Rank Representation and Superpixel Segmentation,
ICIP16(3086-3090)
IEEE DOI 1610
Gaussian noise BibRef

Rizkinia, M., Baba, T., Shirai, K.[Keiichiro], Okuda, M.[Masahiro],
Local Spectral Component Decomposition for Multi-Channel Image Denoising,
IP(25), No. 7, July 2016, pp. 3208-3218.
IEEE DOI 1606
hyperspectral imaging BibRef

Shirai, K.[Keiichiro], Okuda, M.[Masahiro], Ikehara, M.[Masaaki],
Color-line vector field and local color component decomposition for smoothing and denoising of color images,
ICPR12(3050-3053).
WWW Link. 1302
BibRef

Xie, Y., Qu, Y., Tao, D., Wu, W., Yuan, Q., Zhang, W.,
Hyperspectral Image Restoration via Iteratively Regularized Weighted Schatten p -Norm Minimization,
GeoRS(54), No. 8, August 2016, pp. 4642-4659.
IEEE DOI 1608
geophysical image processing BibRef

Liu, S.[Shuai], Jiao, L.C.[Li-Cheng], Yang, S.Y.[Shu-Yuan], Liu, H.Y.[Hong-Ying],
Hierarchical Sparse Bayesian Learning with Beta Process Priors for Hyperspectral Imagery Restoration,
IEICE(E100-D), No. 1, February 2017, pp. 350-358.
WWW Link. 1702
BibRef

Priego, B.[Blanca], Duro, R.J.[Richard J.], Chanussot, J.[Jocelyn],
4DCAF: A temporal approach for denoising hyperspectral image sequences,
PR(72), No. 1, 2017, pp. 433-445.
Elsevier DOI 1708
Hyperspectral BibRef

Chen, Y., Guo, Y., Wang, Y., Wang, D., Peng, C., He, G.,
Denoising of Hyperspectral Images Using Nonconvex Low Rank Matrix Approximation,
GeoRS(55), No. 9, September 2017, pp. 5366-5380.
IEEE DOI 1709
augmented Lagrangian multipliers method, Gaussian noise. BibRef

Shukla, U.P.[Urvashi Prakash], Nanda, S.J.[Satyasai Jagannath],
Denoising hyperspectral images using Hilbert vibration decomposition with cluster validation,
IET-IPR(12), No. 10, October 2018, pp. 1736-1745.
DOI Link 1809
BibRef

Sun, L.[Le], Zhan, T.M.[Tian-Ming], Wu, Z.[Zebin], Jeon, B.W.[Byeung-Woo],
A Novel 3D Anisotropic Total Variation Regularized Low Rank Method for Hyperspectral Image Mixed Denoising,
IJGI(7), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef
Earlier: A1, A4, Only:
A novel subspace spatial-spectral low rank learning method for hyperspectral denoising,
VCIP17(1-4)
IEEE DOI 1804
hyperspectral imaging, image denoising, image representation, image restoration, iterative methods, subspace low rank BibRef

Yue, Z.S.[Zong-Sheng], Meng, D.Y.[De-Yu], Sun, Y.Q.[Yong-Qing], Zhao, Q.[Qian],
Hyperspectral Image Restoration under Complex Multi-Band Noises,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Chang, Y., Yan, L., Fang, H., Zhong, S., Liao, W.,
HSI-DeNet: Hyperspectral Image Restoration via Convolutional Neural Network,
GeoRS(57), No. 2, February 2019, pp. 667-682.
IEEE DOI 1901
Image restoration, Correlation, Data models, Noise reduction, Tensile stress, Convolution, Task analysis, hyperspectral image (HSI) restoration BibRef

Wei, W., Zhang, L., Jiao, Y., Tian, C., Wang, C., Zhang, Y.,
Intracluster Structured Low-Rank Matrix Analysis Method for Hyperspectral Denoising,
GeoRS(57), No. 2, February 2019, pp. 866-880.
IEEE DOI 1901
Noise reduction, Matrix decomposition, Periodic structures, Sparse matrices, Hyperspectral imaging, Correlation, singular value decomposition (SVD) BibRef

Zheng, X., Yuan, Y., Lu, X.,
Hyperspectral Image Denoising by Fusing the Selected Related Bands,
GeoRS(57), No. 5, May 2019, pp. 2596-2609.
IEEE DOI 1905
approximation theory, correlation methods, geophysical image processing, hyperspectral imaging, image fusion BibRef

Liu, W., Lee, J.,
A 3-D Atrous Convolution Neural Network for Hyperspectral Image Denoising,
GeoRS(57), No. 8, August 2019, pp. 5701-5715.
IEEE DOI 1908
convolutional neural nets, feature extraction, geophysical image processing, hyperspectral imaging, multiscale structure BibRef

Zheng, Y., Huang, T., Zhao, X., Jiang, T., Ma, T., Ji, T.,
Mixed Noise Removal in Hyperspectral Image via Low-Fibered-Rank Regularization,
GeoRS(58), No. 1, January 2020, pp. 734-749.
IEEE DOI 2001
Noise reduction, Correlation, Mathematical model, TV, Numerical models, Gaussian noise, tensor nuclear norm BibRef

Maffei, A., Haut, J.M., Paoletti, M.E., Plaza, J., Bruzzone, L., Plaza, A.,
A Single Model CNN for Hyperspectral Image Denoising,
GeoRS(58), No. 4, April 2020, pp. 2516-2529.
IEEE DOI 2004
Noise reduction, Data models, Hyperspectral imaging, Correlation, Task analysis, Gray-scale, Convolutional neural networks (CNNs), spatial-spectral information BibRef

Chen, Y., He, W., Yokoya, N., Huang, T.,
Hyperspectral Image Restoration Using Weighted Group Sparsity-Regularized Low-Rank Tensor Decomposition,
Cyber(50), No. 8, August 2020, pp. 3556-3570.
IEEE DOI 2007
Image restoration, TV, Matrix decomposition, Correlation, Hyperspectral imaging, Noise measurement, low-rank tensor decomposition BibRef

Zeng, H.J.[Hai-Jin], Xie, X.Z.[Xiao-Zhen], Cui, H.J.[Hao-Jie], Zhao, Y.[Yuan], Ning, J.F.[Ji-Feng],
Hyperspectral image restoration via CNN denoiser prior regularized low-rank tensor recovery,
CVIU(197-198), 2020, pp. 103004.
Elsevier DOI 2008
Hyperspectral image (HSI), Low-rank tensor decomposition, Plug- and-play, Deep prior, Restoration BibRef

Takeyama, S.[Saori], Ono, S.[Shunsuke], Kumazawa, I.[Itsuo],
A Constrained Convex Optimization Approach to Hyperspectral Image Restoration with Hybrid Spatio-Spectral Regularization,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Aetesam, H.[Hazique], Poonam, K.[Kumari], Maji, S.K.[Suman Kumar],
Proximal approach to denoising hyperspectral images under mixed-noise model,
IET-IPR(14), No. 14, December 2020, pp. 3366-3372.
DOI Link 2012
BibRef

Wang, M., Wang, Q., Chanussot, J., Li, D.,
Hyperspectral Image Mixed Noise Removal Based on Multidirectional Low-Rank Modeling and Spatial-Spectral Total Variation,
GeoRS(59), No. 1, January 2021, pp. 488-507.
IEEE DOI 2012
Tensile stress, Noise reduction, TV, Gaussian noise, Minimization, Matrix decomposition, Hyperspectral sensors, weighted sum of weighted tensor nuclear norm minimization (WSWTNNM) BibRef

Deng, L.[Lei], Sun, J.[Jie], Chen, Y.[Yong], Lu, H.[Han], Duan, F.Z.[Fu-Zhou], Zhu, L.[Lin], Fan, T.X.[Tian-Xing],
M2H-Net: A Reconstruction Method For Hyperspectral Remotely Sensed Imagery,
PandRS(173), 2021, pp. 323-348.
Elsevier DOI 2102
Hyperspectral, Reconstruction, Deep learning, M2H-Net, GF-5, Remote sensing BibRef

Yuzuriha, R.[Ryota], Kurihara, R.[Ryuji], Matsuoka, R.[Ryo], Okuda, M.[Masahiro],
TNNG: Total Nuclear Norms of Gradients for Hyperspectral Image Prior,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Sarkar, S.[Sourish], Sahay, R.R.[Rajiv Ranjan],
A Non-Local Superpatch-Based Algorithm Exploiting Low Rank Prior for Restoration of Hyperspectral Images,
IP(30), 2021, pp. 6335-6348.
IEEE DOI 2107
Image restoration, Sparse matrices, Hyperspectral imaging, Degradation, Additives, Noise reduction, Gaussian noise, structural similarity index measure BibRef

He, W.[Wei], Yao, Q.M.[Quan-Ming], Li, C.[Chao], Yokoya, N.[Naoto], Zhao, Q.B.[Qi-Bin],
Non-Local Meets Global: An Integrated Paradigm for Hyperspectral Denoising,
CVPR19(6861-6870).
IEEE DOI 2002
BibRef

Han, C.[Chang], Sang, N.[Nong], Gao, C.X.[Chang-Xin],
A hyperspectral image restoration method based on analysis sparse filter,
ICPR16(769-774)
IEEE DOI 1705
Hyperspectral imaging, Image reconstruction, Image restoration, Noise reduction, TV BibRef

Teng, Y., Zhang, Y., Ti, C.,
A novel multi-scale LRMR method for hyperspectral images restoration,
ICIP16(1988-1992)
IEEE DOI 1610
Decision support systems BibRef

Wang, M.[Mengdi], Yu, J.[Jing], Sun, W.D.[Wei-Dong],
Group-based hyperspectral image denoising using low rank representation,
ICIP15(1623-1627)
IEEE DOI 1512
Denoising BibRef

Lam, A.[Antony], Sato, I.[Imari], Sato, Y.[Yoichi],
Denoising hyperspectral images using spectral domain statistics,
ICPR12(477-480).
WWW Link. 1302
BibRef

Zhang, Y.F.[Yi-Fan], Duijster, A.[Arno], Scheunders, P.[Paul],
A hyperspectral image restoration technique,
ICIP09(2873-2876).
IEEE DOI 0911
BibRef

Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Least Squares Applied to Restoration .