Hyperspectral Image Denoising Based on Nonconvex Low-Rank Tensor Approximation and lp Norm Regularization

A new nonconvex smooth rank approximation model is proposed to deal with HSI mixed noise in this paper. The low-rank matrix with Laplace function regularization is used to approximate the nuclear norm, and its performance is superior to the nuclear norm regularization. A new phase congruency lp norm...

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Autores principales:	Li Bo, Luo Xuegang, Lv Junrui
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/cf4a0f7d0606480f834f1e7ee2f3b4ac
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spelling	oai:doaj.org-article:cf4a0f7d0606480f834f1e7ee2f3b4ac2021-11-15T01:19:51ZHyperspectral Image Denoising Based on Nonconvex Low-Rank Tensor Approximation and lp Norm Regularization1563-514710.1155/2021/4500957https://doaj.org/article/cf4a0f7d0606480f834f1e7ee2f3b4ac2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4500957https://doaj.org/toc/1563-5147A new nonconvex smooth rank approximation model is proposed to deal with HSI mixed noise in this paper. The low-rank matrix with Laplace function regularization is used to approximate the nuclear norm, and its performance is superior to the nuclear norm regularization. A new phase congruency lp norm model is proposed to constrain the spatial structure information of hyperspectral images, to solve the phenomenon of “artificial artifact” in the process of hyperspectral image denoising. This model not only makes use of the low-rank characteristic of the hyperspectral image accurately, but also combines the structural information of all bands and the local information of the neighborhood, and then based on the Alternating Direction Method of Multipliers (ADMM), an optimization method for solving the model is proposed. The results of simulation and real data experiments show that the proposed method is more effective than the competcing state-of-the-art denoising methods.Li BoLuo XuegangLv JunruiHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040MathematicsQA1-939ENMathematical Problems in Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 Li Bo Luo Xuegang Lv Junrui Hyperspectral Image Denoising Based on Nonconvex Low-Rank Tensor Approximation and lp Norm Regularization
description	A new nonconvex smooth rank approximation model is proposed to deal with HSI mixed noise in this paper. The low-rank matrix with Laplace function regularization is used to approximate the nuclear norm, and its performance is superior to the nuclear norm regularization. A new phase congruency lp norm model is proposed to constrain the spatial structure information of hyperspectral images, to solve the phenomenon of “artificial artifact” in the process of hyperspectral image denoising. This model not only makes use of the low-rank characteristic of the hyperspectral image accurately, but also combines the structural information of all bands and the local information of the neighborhood, and then based on the Alternating Direction Method of Multipliers (ADMM), an optimization method for solving the model is proposed. The results of simulation and real data experiments show that the proposed method is more effective than the competcing state-of-the-art denoising methods.
format	article
author	Li Bo Luo Xuegang Lv Junrui
author_facet	Li Bo Luo Xuegang Lv Junrui
author_sort	Li Bo
title	Hyperspectral Image Denoising Based on Nonconvex Low-Rank Tensor Approximation and lp Norm Regularization
title_short	Hyperspectral Image Denoising Based on Nonconvex Low-Rank Tensor Approximation and lp Norm Regularization
title_full	Hyperspectral Image Denoising Based on Nonconvex Low-Rank Tensor Approximation and lp Norm Regularization
title_fullStr	Hyperspectral Image Denoising Based on Nonconvex Low-Rank Tensor Approximation and lp Norm Regularization
title_full_unstemmed	Hyperspectral Image Denoising Based on Nonconvex Low-Rank Tensor Approximation and lp Norm Regularization
title_sort	hyperspectral image denoising based on nonconvex low-rank tensor approximation and lp norm regularization
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/cf4a0f7d0606480f834f1e7ee2f3b4ac
work_keys_str_mv	AT libo hyperspectralimagedenoisingbasedonnonconvexlowranktensorapproximationandlpnormregularization AT luoxuegang hyperspectralimagedenoisingbasedonnonconvexlowranktensorapproximationandlpnormregularization AT lvjunrui hyperspectralimagedenoisingbasedonnonconvexlowranktensorapproximationandlpnormregularization
_version_	1718428950264283136

Hyperspectral Image Denoising Based on Nonconvex Low-Rank Tensor Approximation and lp Norm Regularization

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