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
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
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
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
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
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
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],
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.[Yulan],
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
Yuan, Q.,
Zhang, Q.,
Li, J.,
Shen, H.,
Zhang, L.,
Hyperspectral Image Denoising Employing a Spatial-Spectral Deep
Residual Convolutional Neural Network,
GeoRS(57), No. 2, February 2019, pp. 1205-1218.
IEEE DOI
1901
Noise reduction, Feature extraction, Noise measurement,
Hyperspectral imaging, Image denoising,
multiscale feature extraction
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
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 .