Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition | |
Wang Y(王尧)1; Peng, Jiangjun1; Zhao Q(赵谦)1; Leung, Yee2; Zhao XL(赵熙乐)3; Meng DY(孟德宇)1,4 | |
Department | 机器人学研究室 |
Source Publication | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
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ISSN | 1939-1404 |
2018 | |
Volume | 11Issue:4Pages:1227-1243 |
Indexed By | SCI ; EI |
EI Accession number | 20180104604831 |
WOS ID | WOS:000429956000018 |
Contribution Rank | 4 |
Funding Organization | National Natural Science Foundation of China ; Vice-Chancellor's Discretionary Fund of the Chinese University of Hong Kong |
Keyword | Hyperspectral Image (Hsi) Low-rank Tensor Decomposition Mixed Noise Total Variation (Tv) |
Abstract | Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise during the acquisition process, e.g., Gaussian noise, impulse noise, dead lines, stripes, etc. Such complex noise could degrade the quality of the acquired HSIs, limiting the precision of the subsequent processing. In this paper, we present a novel tensor-based HSI restoration approach by fully identifying the intrinsic structures of the clean HSI part and the mixed noise part. Specifically, for the clean HSI part, we use tensor Tucker decomposition to describe the global correlation among all bands, and an anisotropic spatial-spectral total variation regularization to characterize the piecewise smooth structure in both spatial and spectral domains. For the mixed noise part, we adopt the l(1) norm regularization to detect the sparse noise, including stripes, impulse noise, and dead pixels. Despite that TV regularization has the ability of removing Gaussian noise, the Frobenius norm term is further used to model heavy Gaussian noise for some real-world scenarios. Then, we develop an efficient algorithm for solving the resulting optimization problem by using the augmented Lagrange multiplier method. Finally, extensive experiments on simulated and real-world noisy HSIs are carried out to demonstrate the superiority of the proposed method over the existing state-of-the-art ones. |
Language | 英语 |
WOS Subject | Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS Keyword | NUCLEAR NORM MINIMIZATION ; TOTAL VARIATION MODEL ; NOISE-REDUCTION ; MATRIX RECOVERY ; SPARSE REPRESENTATION ; CLASSIFICATION |
WOS Research Area | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
Funding Project | National Natural Science Foundation of China[11501440] ; National Natural Science Foundation of China[61603292] ; National Natural Science Foundation of China[61673015] ; National Natural Science Foundation of China[61373114] ; National Natural Science Foundation of China[61402082] ; National Natural Science Foundation of China[61772003] ; Vice-Chancellor's Discretionary Fund of the Chinese University of Hong Kong |
Citation statistics | |
Document Type | 期刊论文 |
Identifier | http://ir.sia.cn/handle/173321/21888 |
Collection | 机器人学研究室 |
Corresponding Author | Zhao Q(赵谦) |
Affiliation | 1.School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, China; 2.Institute of Future Cities, The Chinese University of Hong Kong, Shatin, Hong Kong, SAR; 3.School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China; 4.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China |
Recommended Citation GB/T 7714 | Wang Y,Peng, Jiangjun,Zhao Q,et al. Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2018,11(4):1227-1243. |
APA | Wang Y,Peng, Jiangjun,Zhao Q,Leung, Yee,Zhao XL,&Meng DY.(2018).Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,11(4),1227-1243. |
MLA | Wang Y,et al."Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 11.4(2018):1227-1243. |
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Hyperspectral Image (2500KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text |
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