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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
Department机器人学研究室
Source PublicationRemote Sensing
ISSN2072-4292
2017
Volume9Issue:12Pages:1-16
Indexed BySCI ; EI
EI Accession number20175104560227
WOS IDWOS:000419235700082
Contribution Rank1
Funding OrganizationNational 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.
KeywordHyperspectral Image Super-resolution Low-rank Tensor Approximation Nonlocal Self-similarity Folded-concave Regularization Total Variation Admm
AbstractHyperspectral 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.
Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectRemote Sensing
WOS KeywordHIGH-RESOLUTION IMAGE ; RECONSTRUCTION ; SPARSE ; SELECTION
WOS Research AreaRemote Sensing
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/21473
Collection机器人学研究室
Corresponding AuthorHan Z(韩志)
Affiliation1.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
Recommended Citation
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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