Mixed Noise Removal for Hyperspectral Images Based on Global Tensor Low-Rankness and Nonlocal SVD-Aided Group Sparsity-Reference-Cited by-同舟云学术

Mixed Noise Removal for Hyperspectral Images Based on Global Tensor Low-Rankness and Nonlocal SVD-Aided Group Sparsity

Published:2023 Issue: Volume:61 Page:1-17
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Sun Le¹^ORCID,Cao Qiujie²,Chen Yuwen³^ORCID,Zheng Yuhui²^ORCID,Wu Zebin⁴^ORCID

Affiliation:

1. School of Computer Science, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology (NUIST), Nanjing, China

2. School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, China

3. Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences (CAS), Chongqing, China

4. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

Funder

National Natural Science Foundation of China

Youth Innovation Promotion Association of the Chinese Academy of Sciences

Natural Science Foundation of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10073634.pdf?arnumber=10073634

Reference57 articles.

1. Weighted Nuclear Norm Minimization with Application to Image Denoising

2. Hyperspectral image restoration by tensor fibered rank constrained optimization and plug-and-play regularization;liu;IEEE Trans Geosci Remote Sens,2021

3. Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering

4. Hyperspectral Mixed Noise Removal By $\ell _1$-Norm-Based Subspace Representation

5. Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

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