Relaxed Low Tensor Train Rank Representation with Structural Smoothness for Hyperspectral Image Super-resolution
文献类型：会议论文
| 作者 | Li, Shengchuan3; Jia HD(贾慧迪)1,2,5 ; Chen XA(陈希爱)2,5; Li, Sun4; Han Z(韩志)2,5; Tang YD(唐延东)2,5; Liu JX(刘佳鑫)3
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| 出版日期 | 2020 |
| 会议日期 | October 10-13, 2020 |
| 会议地点 | Xi'an, China |
| 关键词 | low tensor train rank log-sum norm nonlocal similarity structural smoothness super-resolution |
| 页码 | 375-380 |
| 英文摘要 | 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. |
| 产权排序 | 2 |
| 会议录 | Proceedings of 10th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2020
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| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-7281-9009-9 |
| WOS记录号 | WOS:000646188000068 |
| 源URL | [http://ir.sia.cn/handle/173321/28164]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Chen XA(陈希爱) |
| 作者单位 | 1.University of Chinese Academy of Sciences, Beijing 100049, China 2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China 3.State Grid Liaoning Electric Power Research Institute, Shenyang 110006, China 4.State Grid Shandong Electric Power Company, Shandong, 250001, China 5.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China |
| 推荐引用方式 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]. 见:. Xi'an, China. October 10-13, 2020. |
入库方式： OAI收割
来源：沈阳自动化研究所
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