Joint weighted nuclear norm and total variation regularization for hyperspectral image denoising-Reference-Cited by-同舟云学术

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Joint weighted nuclear norm and total variation regularization for hyperspectral image denoising

Published:2017-10-01 Issue:2 Volume:39 Page:334-355
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Du Bo¹,Huang Zhiqiang¹,Wang Nan²,Zhang Yuxiang³,Jia Xiuping⁴

Affiliation:

1. School of Computer, Wuhan University, Wuhan, China

2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China

3. Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, China

4. School of Engineering and Information Technology, University of New South Wales, Sydney, Australia

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hubei Province

Fundamental Research Funds for the Central Universities

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2017.1382742

Reference41 articles.

1. Hyperspectral Image Denoising Using Spatio-Spectral Total Variation

2. Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems

3. Hyperspectral Subspace Identification

4. Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

5. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

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