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
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,
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.B.[Ze-Bin],
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.J.[Ryu-Ji],
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
Kong, W.F.[Wen-Feng],
Song, Y.Y.[Yang-Yang],
Liu, J.[Jing],
Hyperspectral Image Denoising via Framelet Transformation Based
Three-Modal Tensor Nuclear Norm,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link
2110
BibRef
Zhuang, L.[Lina],
Ng, M.K.[Michael K.],
Fu, X.[Xiyou],
Hyperspectral Image Mixed Noise Removal Using Subspace Representation
and Deep CNN Image Prior,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link
2110
BibRef
Hu, T.,
Li, W.,
Liu, N.,
Tao, R.,
Zhang, F.,
Scheunders, P.,
Hyperspectral Image Restoration Using Adaptive Anisotropy Total
Variation and Nuclear Norms,
GeoRS(59), No. 2, February 2021, pp. 1516-1533.
IEEE DOI
2101
Noise reduction, Image restoration, Gaussian noise, TV,
Anisotropic magnetoresistance, Adaptation models, weighted nuclear norm
BibRef
Liu, Y.Y.[Yun-Yang],
Zhao, X.L.[Xi-Le],
Zheng, Y.B.[Yu-Bang],
Ma, T.H.[Tian-Hui],
Zhang, H.Y.[Hong-Yan],
Hyperspectral Image Restoration by Tensor Fibered Rank Constrained
Optimization and Plug-and-Play Regularization,
GeoRS(60), 2022, pp. 1-17.
IEEE DOI
2112
Tensors, Image restoration, Hyperspectral imaging, Correlation,
Periodic structures, Electron tubes, TV,
three-directional randomized tensor singular value decomposition (3DRT-SVD)
See also Weighted Low-Rank Tensor Recovery for Hyperspectral Image Restoration.
See also Double-Factor-Regularized Low-Rank Tensor Factorization for Mixed Noise Removal in Hyperspectral Image.
BibRef
He, W.[Wei],
Yao, Q.M.[Quan-Ming],
Li, C.[Chao],
Yokoya, N.[Naoto],
Zhao, Q.B.[Qi-Bin],
Zhang, H.Y.[Hong-Yan],
Zhang, L.P.[Liang-Pei],
Non-Local Meets Global: An Iterative Paradigm for Hyperspectral Image
Restoration,
PAMI(44), No. 4, April 2022, pp. 2089-2107.
IEEE DOI
2203
BibRef
Earlier: A1, A2, A3, A4, A5, Only:
Non-Local Meets Global:
An Integrated Paradigm for Hyperspectral Denoising,
CVPR19(6861-6870).
IEEE DOI
2002
Image restoration, Noise reduction, Tensile stress, Correlation,
Task analysis, Image reconstruction, Image coding,
low-rank tensor
BibRef
Zhang, J.J.[Jun-Jie],
Cai, Z.Y.[Zhou-Yin],
Chen, F.S.[Fan-Sheng],
Zeng, D.[Dan],
Hyperspectral Image Denoising via Adversarial Learning,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Sun, H.Z.[He-Zhi],
Zheng, K.[Ke],
Liu, M.[Ming],
Li, C.[Chao],
Yang, D.[Dong],
Li, J.D.[Jin-Dong],
Hyperspectral Image Mixed Noise Removal Using a Subspace Projection
Attention and Residual Channel Attention Network,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Zhou, L.J.[Li-Jian],
Xu, E.[Erya],
Hao, S.Y.[Si-Yuan],
Ye, Y.X.[Yuan-Xin],
Zhao, K.[Kun],
Data-Wise Spatial Regional Consistency Re-Enhancement for
Hyperspectral Image Classification,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Zhou, L.J.[Li-Jian],
Ma, X.Y.[Xiao-Yu],
Wang, X.L.[Xi-Liang],
Hao, S.Y.[Si-Yuan],
Ye, Y.X.[Yuan-Xin],
Zhao, K.[Kun],
Shallow-to-Deep Spatial-Spectral Feature Enhancement for
Hyperspectral Image Classification,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link
2301
BibRef
Qin, J.C.[Jin-Chun],
Zhao, H.R.[Hong-Rui],
Liu, B.[Bing],
Self-Supervised Denoising for Real Satellite Hyperspectral Imagery,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Dou, H.X.[Hong-Xia],
Pan, X.M.[Xiao-Miao],
Wang, C.[Chao],
Shen, H.Z.[Hao-Zhen],
Deng, L.J.[Liang-Jian],
Spatial and Spectral-Channel Attention Network for Denoising on
Hyperspectral Remote Sensing Image,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Xiong, F.C.[Feng-Chao],
Zhou, J.[Jun],
Tao, S.[Shuyin],
Lu, J.F.[Jian-Feng],
Zhou, J.T.[Jian-Tao],
Qian, Y.T.[Yun-Tao],
SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral
Image Denoising,
IP(31), 2022, pp. 5469-5483.
IEEE DOI
2208
Noise reduction, Noise measurement, Correlation, Neural networks,
Training, Tensors, Sensors, Hyperspectral image denoising,
multidimensional sparse representation
BibRef
Pang, L.[Li],
Gu, W.Z.[Wei-Zhen],
Cao, X.Y.[Xiang-Yong],
TRQ3DNet: A 3D Quasi-Recurrent and Transformer Based Network for
Hyperspectral Image Denoising,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Zhang, T.[Tao],
Fu, Y.[Ying],
Zhang, J.[Jun],
Guided Hyperspectral Image Denoising with Realistic Data,
IJCV(130), No. 11, November 2022, pp. 2885-2901.
Springer DOI
2210
BibRef
Zhang, T.[Tao],
Fu, Y.[Ying],
Li, C.[Cheng],
Hyperspectral Image Denoising with Realistic Data,
ICCV21(2228-2237)
IEEE DOI
2203
Parameter estimation, Computational modeling, Noise reduction,
Cameras, Data models, Noise measurement, Computational photography,
Low-level and physics-based vision
BibRef
Sun, L.[Le],
He, C.X.[Cheng-Xun],
Zheng, Y.H.[Yu-Hui],
Wu, Z.B.[Ze-Bin],
Jeon, B.W.[Byeung-Woo],
Tensor Cascaded-Rank Minimization in Subspace:
A Unified Regime for Hyperspectral Image Low-Level Vision,
IP(32), 2023, pp. 100-115.
IEEE DOI
2301
Tensors, Task analysis, Image reconstruction, Noise reduction,
Image restoration, Correlation, Computational modeling,
low-rank tensor representation
BibRef
Wei, X.[Xing],
Xiao, J.H.[Jia-Hua],
Gong, Y.H.[Yi-Hong],
Blind Hyperspectral Image Denoising with Degradation Information
Learning,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link
2301
BibRef
Gkillas, A.[Alexandros],
Ampeliotis, D.[Dimitris],
Berberidis, K.[Kostas],
Connections Between Deep Equilibrium and Sparse Representation Models
With Application to Hyperspectral Image Denoising,
IP(32), 2023, pp. 1513-1528.
IEEE DOI
2303
Encoding, Sparse matrices, Image coding, Data models,
Hyperspectral imaging, Computational modeling, Optimization
BibRef
Wang, Y.F.[Yi-Fan],
Xu, S.[Shuang],
Cao, X.Y.[Xiang-Yong],
Ke, Q.[Qiao],
Ji, T.Y.[Teng-Yu],
Zhu, X.X.[Xiang-Xiang],
Hyperspectral Denoising Using Asymmetric Noise Modeling Deep Image
Prior,
RS(15), No. 8, 2023, pp. 1970.
DOI Link
2305
BibRef
Pan, E.[Erting],
Ma, Y.[Yong],
Mei, X.G.[Xiao-Guang],
Fan, F.[Fan],
Ma, J.Y.[Jia-Yi],
Hyperspectral image denoising via spectral noise distribution
bootstrap,
PR(142), 2023, pp. 109699.
Elsevier DOI
2307
Hyperspectral image denoising, Image restoration,
Spectral distribution, Noise estimation, Noise distribution
BibRef
Lian, X.Y.[Xiao-Ying],
Yin, Z.[Zhonghai],
Zhao, S.W.[Si-Wei],
Li, D.D.[Dan-Dan],
Lv, S.[Shuai],
Pang, B.[Boyu],
Sun, D.[Dexin],
A Neural Network for Hyperspectral Image Denoising by Combining
Spatial-Spectral Information,
RS(15), No. 21, 2023, pp. 5174.
DOI Link
2311
BibRef
Han, J.[Jie],
Pan, C.[Chuang],
Ding, H.Y.[Hai-Yong],
Zhang, Z.C.[Zhi-Chao],
Double-Factor Tensor Cascaded-Rank Decomposition for Hyperspectral
Image Denoising,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link
2401
BibRef
Li, S.Z.[Shou-Zhi],
Geng, X.[Xiurui],
Zhu, L.L.[Liang-Liang],
Ji, L.[Luyan],
Zhao, Y.C.[Yong-Chao],
Hyperspectral Image Denoising Based on Principal-Third-Order-Moment
Analysis,
RS(16), No. 2, 2024, pp. 276.
DOI Link
2402
BibRef
Chen, Y.R.[Yu-Rong],
Zhang, H.[Hui],
Wang, Y.N.[Yao-Nan],
Yang, Y.M.[Yi-Min],
Wu, J.[Jonathan],
Flex-DLD: Deep Low-Rank Decomposition Model with Flexible Priors for
Hyperspectral Image Denoising and Restoration,
IP(33), 2024, pp. 1211-1226.
IEEE DOI
2402
Noise reduction, Optimization, Hyperspectral imaging,
Image restoration, Tensors, Noise measurement, self-supervised learning
BibRef
Zhang, J.[Jing],
Zheng, R.J.[Ren-Jie],
Wan, Z.[Zekang],
Geng, R.J.[Rui-Jing],
Wang, Y.[Yi],
Yang, Y.[Yu],
Zhang, X.P.[Xue-Peng],
Li, Y.S.[Yun-Song],
Hyperspectral Image Super-Resolution Based on Feature Diversity
Extraction,
RS(16), No. 3, 2024, pp. 436.
DOI Link
2402
BibRef
Zhang, J.[Jing],
Chen, L.[Lu],
Zhuo, L.[Li],
Liang, X.[Xi],
Li, J.F.[Jia-Feng],
An Efficient Hyperspectral Image Retrieval Method: Deep
Spectral-Spatial Feature Extraction with DCGAN and Dimensionality
Reduction Using t-SNE-Based NM Hashing,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link
1804
BibRef
Xu, P.[Ping],
Liu, L.[Lei],
Zheng, H.F.[Hai-Feng],
Yuan, X.[Xin],
Xu, C.[Chen],
Xue, L.Y.[Ling-Yun],
Degradation-Aware Dynamic Fourier-Based Network for Spectral
Compressive Imaging,
MultMed(26), 2024, pp. 2838-2850.
IEEE DOI
2402
Image reconstruction, Degradation, Feature extraction, Imaging,
Mathematical models, Heuristic algorithms, Convolution,
snapshot compressive imaging
BibRef
Wu, Y.J.[Yong-Jie],
Xu, W.[Wei],
Zheng, L.L.[Liang-Liang],
Hyperspectral Image Mixed Noise Removal via Double Factor Total
Variation Nonlocal Low-Rank Tensor Regularization,
RS(16), No. 10, 2024, pp. 1686.
DOI Link
2405
BibRef
Dong, L.[Le],
Mo, Y.[Yige],
Sun, H.[Hao],
Wu, F.F.[Fang-Fang],
Dong, W.S.[Wei-Sheng],
Memory Augmentation and Non-Local Spectral Attention for
Hyperspectral Denoising,
RS(16), No. 11, 2024, pp. 1937.
DOI Link
2406
BibRef
Mohan, D.[Divya],
Aravinth, J.,
Rajendran, S.[Sankaran],
Hyperspectral Image Denoising and Compression Using Optimized
Bidirectional Gated Recurrent Unit,
RS(16), No. 17, 2024, pp. 3258.
DOI Link
2409
BibRef
Luo, Z.Z.[Zhao-Zhi],
Wang, X.Y.[Xin-Yu],
Pellikka, P.[Petri],
Heiskanen, J.[Janne],
Zhong, Y.F.[Yan-Fei],
Unsupervised Adaptation Learning for Real Multiplatform Hyperspectral
Image Denoising,
Cyber(54), No. 10, October 2024, pp. 5781-5794.
IEEE DOI
2410
Noise reduction, Noise, Hyperspectral imaging, Noise measurement,
Adaptation models, Transfer learning, Computational modeling,
unsupervised adaptation learning (UAL)
BibRef
Li, M.[Miaoyu],
Fu, Y.[Ying],
Zhang, T.[Tao],
Liu, J.[Ji],
Dou, D.[Dejing],
Yan, C.G.[Cheng-Gang],
Zhang, Y.[Yulun],
Latent Diffusion Enhanced Rectangle Transformer for Hyperspectral
Image Restoration,
PAMI(47), No. 1, January 2025, pp. 549-564.
IEEE DOI
2412
BibRef
Earlier: A1, A4, A2, A7, A5, Only:
Spectral Enhanced Rectangle Transformer for Hyperspectral Image
Denoising,
CVPR23(5805-5814)
IEEE DOI
2309
Image restoration, Transformers, Noise reduction,
Hyperspectral imaging, Degradation, Diffusion models,
transformer
BibRef
Xing, Z.Y.[Zhong-Yang],
Wang, H.Q.[Hao-Qian],
Liu, J.[Ju],
Cheng, X.[Xiangai],
Xu, Z.J.[Zhong-Jie],
MambaHR: State Space Model for Hyperspectral Image Restoration Under
Stray Light Interference,
RS(16), No. 24, 2024, pp. 4661.
DOI Link
2501
BibRef
Ding, L.[Ling],
Wang, Q.[Qiong],
Poo, Y.[Yin],
Zhang, X.G.[Xing-Gan],
Nonlocal Gaussian scale mixture modeling for hyperspectral image
denoising,
CVIU(251), 2025, pp. 104270.
Elsevier DOI
2501
Hyperspectral image denoising, Nonlocal self-similarity,
Nonlocal Gaussian scale mixture, Optimization
BibRef
Zeng, H.J.[Hai-Jin],
Cao, J.Z.[Jie-Zhang],
Zhang, K.[Kai],
Chen, Y.Y.[Yong-Yong],
Luong, H.[Hiep],
Philips, W.[Wilfried],
Unmixing Diffusion for Self-Supervised Hyperspectral Image Denoising,
CVPR24(27820-27830)
IEEE DOI
2410
Degradation, Training, Noise reduction, Transformers,
Probability distribution, Noise measurement
BibRef
Li, M.[Miaoyu],
Fu, Y.[Ying],
Liu, J.[Ji],
Zhang, Y.[Yulun],
Pixel Adaptive Deep Unfolding Transformer for Hyperspectral Image
Reconstruction,
ICCV23(12913-12922)
IEEE DOI Code:
WWW Link.
2401
BibRef
Yu, D.B.[Da-Bing],
Li, Q.W.[Qing-Wu],
Wang, X.L.[Xiao-Lin],
Zhang, Z.L.[Zhi-Liang],
Qian, Y.X.[Yi-Xi],
Xu, C.[Chang],
DSTrans: Dual-Stream Transformer for Hyperspectral Image Restoration,
WACV23(3728-3738)
IEEE DOI
2302
Training, Visualization, Source coding, Superresolution,
Noise reduction, Transformer cores, Transformers,
Agriculture
BibRef
Liu, Y.[Yang],
Zhang, Q.[Qian],
Chen, Y.Y.[Yong-Yong],
Cheng, Q.[Qiang],
Peng, C.[Chong],
Hyperspectral Image Denoising With Log-Based Robust PCA,
ICIP21(1634-1638)
IEEE DOI
2201
Closed-form solutions, Noise reduction, Sparse representation,
Task analysis, Hyperspectral imaging, Principal component analysis
BibRef
Rui, X.Y.[Xiang-Yu],
Cao, X.Y.[Xiang-Yong],
Xie, Q.[Qi],
Yue, Z.S.[Zong-Sheng],
Zhao, Q.[Qian],
Meng, D.Y.[De-Yu],
Learning An Explicit Weighting Scheme for Adapting Complex HSI Noise,
CVPR21(6735-6744)
IEEE DOI
2111
Hyperspectral Image.
Training, Adaptation models, Computational modeling,
Noise reduction, Inference algorithms
BibRef
Takeyama, S.,
Ono, S.,
Kumazawa, I.,
Mixed Noise Removal for Hyperspectral Images Using Hybrid
Spatio-Spectral Total Variati,
ICIP19(3128-3132)
IEEE DOI
1910
hyperspectral image, mixed noise removal, ADMM
BibRef
Itasaka, T.[Tatsuki],
Okuda, M.[Masahiro],
Zero-Shot Hyperspectral Image Denoising With Self-Completion with
Patterned Masks,
ICIP23(1340-1344)
IEEE DOI
2312
BibRef
Imamura, R.,
Itasaka, T.,
Okuda, M.,
Zero-Shot Hyperspectral Image Denoising With Separable Image Prior,
MDALC19(1416-1420)
IEEE DOI
2004
convolutional neural nets, geophysical image processing,
hyperspectral imaging, image colour analysis, image denoising,
Image Restoration
BibRef
Wang, M.,
Yu, J.,
Sun, W.,
LRR-based hyperspectral image restoration by exploiting the union
structure of spectral space and with robust dictionary estimation,
ICIP17(4287-4291)
IEEE DOI
1803
Dictionaries, Estimation, Gaussian noise, Hyperspectral imaging,
Image restoration, Noise measurement, Robustness,
robust principle component analysis
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 .