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Hyperspectral image super-resolution via nonlocal low-rank tensor approximation and total variation regularization
Wang Y(王尧); Chen XA(陈希爱); Han Z(韩志); He, Shiying
作者部门机器人学研究室
关键词Hyperspectral Image Super-resolution Low-rank Tensor Approximation Nonlocal Self-similarity Folded-concave Regularization Total Variation Admm
发表期刊Remote Sensing
ISSN2072-4292
2017
卷号9期号:12页码:1-16
收录类别SCI ; EI
EI收录号20175104560227
WOS记录号WOS:000419235700082
产权排序1
资助机构National Science Foundation of China (Grant Nos. 11501440, 61773367) and the China Postdoctoral Science Foundation (Grant No. 2017M610628). The authors also thank the support from the Youth Innovation Promotion Association CAS.
摘要Hyperspectral image (HSI) possesses three intrinsic characteristics: the global correlation across spectral domain, the nonlocal self-similarity across spatial domain, and the local smooth structure across both spatial and spectral domains. This paper proposes a novel tensor based approach to handle the problem of HSI spatial super-resolution by modeling such three underlying characteristics. Specifically, a noncovex tensor penalty is used to exploit the former two intrinsic characteristics hidden in several 4D tensors formed by nonlocal similar patches within the 3D HSI. In addition, the local smoothness in both spatial and spectral modes of the HSI cube is characterized by a 3D total variation (TV) term. Then, we develop an effective algorithm for solving the resulting optimization by using the local linear approximation (LLA) strategy and the alternative direction method of multipliers (ADMM). A series of experiments are carried out to illustrate the superiority of the proposed approach over some state-of-the-art approaches.
语种英语
WOS标题词Science & Technology ; Technology
WOS类目Remote Sensing
关键词[WOS]HIGH-RESOLUTION IMAGE ; RECONSTRUCTION ; SPARSE ; SELECTION
WOS研究方向Remote Sensing
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文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/21473
专题机器人学研究室
通讯作者Han Z(韩志)
作者单位1.School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
推荐引用方式
GB/T 7714
Wang Y,Chen XA,Han Z,et al. Hyperspectral image super-resolution via nonlocal low-rank tensor approximation and total variation regularization[J]. Remote Sensing,2017,9(12):1-16.
APA Wang Y,Chen XA,Han Z,&He, Shiying.(2017).Hyperspectral image super-resolution via nonlocal low-rank tensor approximation and total variation regularization.Remote Sensing,9(12),1-16.
MLA Wang Y,et al."Hyperspectral image super-resolution via nonlocal low-rank tensor approximation and total variation regularization".Remote Sensing 9.12(2017):1-16.
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