Hyperspectral Image Restoration via Spatial-Spectral Residual Total Variation Regularized Low-Rank Tensor Decomposition

Hyperspectral Image Restoration via Spatial-Spectral Residual Total Variation Regularized Low-Rank Tensor Decomposition

To eliminate the mixed noise in hyperspectral images (HSIs), three-dimensional total variation (3DTV) regularization has been proven as an efficient tool. However, 3DTV regularization is prone to losing image details in restoration. To resolve this issue, we proposed a novel TV, named spatial domain...

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Bibliographic Details
Main Authors:	Xiangyang Kong, Yongqiang Zhao, Jonathan Cheung-Wai Chan, Jize Xue
Format:	Article
Language:	English
Published:	MDPI AG 2022-01-01
Series:	Remote Sensing
Subjects:	hyperspectral image restoration low-rank tensor decomposition SSRTV alternating direction methods of multipliers (ADMM)
Online Access:	https://www.mdpi.com/2072-4292/14/3/511

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