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Tensor Nuclear Norm-Based Low-Rank Approximation With Total Variation Regularization
Chen, Yongyong1; Wang, Shuqin2; Zhou, Yicong1
2018-12
Source PublicationIEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
ISSN1932-4553
Volume12Issue:6Pages:1364-1377
AbstractSome existing low-rank approximation approaches either need to predefine the rank values (such as the matrix/tensor factorization-based methods) or fail to consider local information of data (e.g., spatial or spectral smooth structure). To overcome these drawbacks, this paper proposes a new model called the tensor nuclear norm-based low-rank approximation with total variation regularization (TLR-TV) for color and multispectral image denoising. TLR-TV uses the tensor nuclear norm to encode the global low-rank prior of tensor data and the total variation regularization to preserve the spatial-spectral continuity in a unified framework. Including the hyper total variation (HTV) and spatial-spectral total variation (SSTV), we propose two TLR-TV-based algorithms, namely TLR-HTV and TLR-SSTV. Using the alternating direction method of multiplier, we further propose two simple algorithms to solve TLR-HTV and TLR-SSTV. Extensive experiments on simulated and real-world noisy images demonstrate that the proposed TLR-HTV and TLR-SSTV outperform the state-of-the-art methods in color and multispectral image denoising in terms of quantitative and qualitative evaluations.
KeywordLow-rank tensor approximation tensor nuclear norm hyper total variation spatial-spectral total variation image denoising
DOI10.1109/JSTSP.2018.2873148
URLView the original
Indexed BySCI
Language英语
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000454221700020
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Univ Macau, Dept Comp & Informat Sci, Macau 999078, Peoples R China;
2.Shandong Univ Sci & Technol, Coll Math & Syst Sci, Qingdao 266590, Peoples R China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Chen, Yongyong,Wang, Shuqin,Zhou, Yicong. Tensor Nuclear Norm-Based Low-Rank Approximation With Total Variation Regularization[J]. IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING,2018,12(6):1364-1377.
APA Chen, Yongyong,Wang, Shuqin,&Zhou, Yicong.(2018).Tensor Nuclear Norm-Based Low-Rank Approximation With Total Variation Regularization.IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING,12(6),1364-1377.
MLA Chen, Yongyong,et al."Tensor Nuclear Norm-Based Low-Rank Approximation With Total Variation Regularization".IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 12.6(2018):1364-1377.
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