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Open AccessArticle

Hyperspectral Image Denoising Using Global Weighted Tensor Norm Minimum and Nonlocal Low-Rank Approximation

Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518057, China
Ministry of Basic Education, Sichuan Engineering Technical College, Deyang 618000, China
Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussel, Belgium
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2281;
Received: 6 August 2019 / Revised: 22 September 2019 / Accepted: 24 September 2019 / Published: 29 September 2019
(This article belongs to the Special Issue Advanced Techniques for Spaceborne Hyperspectral Remote Sensing)
A hyperspectral image (HSI) contains abundant spatial and spectral information, but it is always corrupted by various noises, especially Gaussian noise. Global correlation (GC) across spectral domain and nonlocal self-similarity (NSS) across spatial domain are two important characteristics for an HSI. To keep the integrity of the global structure and improve the details of the restored HSI, we propose a global and nonlocal weighted tensor norm minimum denoising method which jointly utilizes GC and NSS. The weighted multilinear rank is utilized to depict the GC information. To preserve structural information with NSS, a patch-group-based low-rank-tensor-approximation (LRTA) model is designed. The LRTA makes use of Tucker decompositions of 4D patches, which are composed of a similar 3D patch group of HSI. The alternating direction method of multipliers (ADMM) is adapted to solve the proposed models. Experimental results show that the proposed algorithm can preserve the structural information and outperforms several state-of-the-art denoising methods. View Full-Text
Keywords: Tucker decomposition; LRTA; nonlocal self-similarity; weighted tensor norm Tucker decomposition; LRTA; nonlocal self-similarity; weighted tensor norm
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MDPI and ACS Style

Kong, X.; Zhao, Y.; Xue, J.; Chan, J. .-W. Hyperspectral Image Denoising Using Global Weighted Tensor Norm Minimum and Nonlocal Low-Rank Approximation. Remote Sens. 2019, 11, 2281.

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