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Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization
He, Shiying; Zhou, Haiwei; Wang Y(王尧); Cao, Wenfei; Han Z(韩志)
作者部门机器人学研究室
会议名称2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
会议日期July 10-15, 2016
会议地点Beijing, China
会议主办者The Institute of Electrical and Electronics Engineers, Geoscience and Remote Sensing Society (GRSS)
会议录名称2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
出版者IEEE
出版地New York
2016
页码6962-6965
收录类别EI ; CPCI(ISTP)
EI收录号20170103213734
WOS记录号WOS:000388114606192
产权排序1
ISBN号978-1-5090-3332-4
关键词Hyperspectral Images Super-resolution Reconstruction Nuclear Norm Folded-concave Penalty 3d Totalvariation
摘要In this paper, we propose a novel approach to hyperspectral image super-resolution by modeling the global spatial-and-spectral correlation and local smoothness properties over hyperspectral images. Specifically, we utilize the tensor nuclear norm and tensor folded-concave penalty functions to describe the global spatial-and-spectral correlation hidden in hyperspectral images, and 3D total variation (TV) to characterize the local spatial-and-spectral smoothness across all hyperspectral bands. Then, we develop an efficient algorithm for solving the resulting optimization problem by combing the local linear approximation (LLA) strategy and alternative direction method of multipliers (ADMM). Experimental results on one hyperspectral image dataset illustrate the merits of the proposed approach.
语种英语
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被引频次:3[WOS]   [WOS记录]     [WOS相关记录]
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/19769
专题机器人学研究室
通讯作者Wang Y(王尧)
作者单位1.School of Mathematics and Statistics, Xi'An Jiaotong University, China
2.Shenyang Institute of Automation, Chinese Academy of Sciences, China
3.School of Mathematics and Information Science, Shaanxi Normal University, China
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He, Shiying,Zhou, Haiwei,Wang Y,et al. Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization[C]//The Institute of Electrical and Electronics Engineers, Geoscience and Remote Sensing Society (GRSS). New York:IEEE,2016:6962-6965.
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