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Remote sensing images destriping using unidirectional hybrid total variation and nonconvex low-rank regularization

  • Published:2020-01 Issue: Volume:363 Page:124-144
  • ISSN:0377-0427
  • Container-title:Journal of Computational and Applied Mathematics
  • language:en
  • Short-container-title:Journal of Computational and Applied Mathematics

Author:

Yang Jing-Hua,Zhao Xi-LeORCID,Ma Tian-Hui,Chen Yong,Huang Ting-ZhuORCID,Ding Meng

Funder

NSFC

Fundamental Research Funds for the Central Universities

National Postdoctoral Program for Innovative Talents

Science Strength Promotion Programme of UESTC

Publisher

Elsevier BV

Subject

Applied Mathematics,Computational Mathematics

Reference49 articles.

1. Graph-regularized low-rank representation for destriping of hyperspectral images;Lu;IEEE Trans. Geosci. Remote Sens.,2013

2. A destriping algorithm based on TV-stokes and unidirectional total variation model;Zhang;Optik,2016

3. A nonlocal weighted joint sparse representation classification method for hyperspectral imagery;Zhang;IEEE J-Stars,2017

4. Segmentation and classification of hyperspectral images using watershed transformation;Tarabalka;Pattern Recognit.,2010

5. An effective graph and depth layer based RGB-D image foreground object extraction method;Xiao;Comput. Vis. Med.,2017

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