Next Article in Journal
Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument
Next Article in Special Issue
A Spectral-Spatial Cascaded 3D Convolutional Neural Network with a Convolutional Long Short-Term Memory Network for Hyperspectral Image Classification
Previous Article in Journal
Multiple-Object-Tracking Algorithm Based on Dense Trajectory Voting in Aerial Videos
Previous Article in Special Issue
Tensor Discriminant Analysis via Compact Feature Representation for Hyperspectral Images Dimensionality Reduction
Open AccessArticle

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

1
Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518057, China
2
Ministry of Basic Education, Sichuan Engineering Technical College, Deyang 618000, China
3
Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussel, Belgium
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2281; https://doi.org/10.3390/rs11192281
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
Show Figures

Graphical abstract

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop