All of Low-Rank and Sparse: A Recast Total Variation Approach to Hyperspectral Denoising-Reference-Cited by-同舟云学术

All of Low-Rank and Sparse: A Recast Total Variation Approach to Hyperspectral Denoising

Published:2023 Issue: Volume:16 Page:7357-7373
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Zeng Haijin¹^ORCID,Huang Shaoguang²^ORCID,Chen Yongyong³^ORCID,Luong Hiep¹^ORCID,Philips Wilfried¹^ORCID

Affiliation:

1. Image Processing and Interpretation, imec Research Group, Ghent University, Gent, Belgium

2. School of Computer Science, China University of Geosciences, Wuhan, China

3. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen, Shenzhen, China

Funder

Flemish Government (AI Research Program), UGent-Special Research Fund

China Scholarship Council

China University of Geosciences, Wuhan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

Reference64 articles.

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