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Relaxed Low Tensor Train Rank Representation with Structural Smoothness for Hyperspectral Image Super-resolution
Li, Shengchuan1; Jia HD(贾慧迪)2,3,4; Chen XA(陈希爱)2,3; Li, Sun5; Han Z(韩志)2,3; Tang YD(唐延东)2,3; Liu, Jiaxin1
Department机器人学研究室
Conference Name10th IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, CYBER 2020
Conference DateOctober 10-13, 2020
Conference PlaceXi'an, China
Source PublicationProceedings of 10th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2020
PublisherIEEE
Publication PlaceNew York
2020
Pages375-380
Indexed ByEI
EI Accession number20210209756057
Contribution Rank2
ISBN978-1-7281-9009-9
Keywordlow tensor train rank log-sum norm nonlocal similarity structural smoothness super-resolution
AbstractWe propose a super-resolution method for hyperspectral image (HSI) that utilizes relaxed low tensor train (TT) rank representation with structural smoothness in this paper. Nonlocal similarity is exploited by grouping the similar HSI cubes. The 4D tensor formed by similar cubes is highly low-rank. The good balanced matricisation scheme of TT and rational shrinkage strategy of log-sum norm motivated us to design the relaxed low TT rank regularization in the model. It can learn the spatial and spectral correlations hidden in these 4-D tensors. The structural smoothness is captured by the three-dimensional total variation (3DTV) regularization in the model. We solve our model via ADMM. Compared with existing state-of-art super-resolution approaches, quantitative and qualitative reconstruct results on typical HSI data indicate that our method is effective.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/28164
Collection机器人学研究室
Corresponding AuthorChen XA(陈希爱)
Affiliation1.State Grid Liaoning Electric Power Research Institute, Shenyang 110006, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China
3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
4.University of Chinese Academy of Sciences, Beijing 100049, China
5.State Grid Shandong Electric Power Company, Shandong, 250001, China
Recommended Citation
GB/T 7714
Li, Shengchuan,Jia HD,Chen XA,et al. Relaxed Low Tensor Train Rank Representation with Structural Smoothness for Hyperspectral Image Super-resolution[C]. New York:IEEE,2020:375-380.
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