| 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
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| Department | 机器人学研究室 |
| Conference Name | 10th IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, CYBER 2020 |
| Conference Date | October 10-13, 2020 |
| Conference Place | Xi'an, China |
| Source Publication | Proceedings of 10th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2020 |
| Publisher | IEEE |
| Publication Place | New York |
| 2020 | |
| Pages | 375-380 |
| Indexed By | EI |
| EI Accession number | 20210209756057 |
| Contribution Rank | 2 |
| ISBN | 978-1-7281-9009-9 |
| Keyword | low tensor train rank log-sum norm nonlocal similarity structural smoothness super-resolution |
| Abstract | We 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 |
| Identifier | http://ir.sia.cn/handle/173321/28164 |
| Collection | 机器人学研究室 |
| Corresponding Author | Chen XA(陈希爱) |
| Affiliation | 1.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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Relaxed Low Tensor T（1123KB） | 会议论文 | 开放获取 | CC BY-NC-SA | View Application Full Text | ||
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