An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization

Chen, T; Xiang, Q; Zhao, D; Sun, L

An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization

Chen, T Xiang, Q Zhao, D Sun, L

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: APPLIED SCIENCES-BASEL, 2023, 13, (12)
Issue Date:: 2023-06

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Field	Value	Language
dc.contributor.author	Chen, T
dc.contributor.author	Xiang, Q
dc.contributor.author	Zhao, D
dc.contributor.author	Sun, L https://orcid.org/0000-0002-9830-1527
dc.date.accessioned	2024-03-28T04:15:53Z
dc.date.available	2024-03-28T04:15:53Z
dc.date.issued	2023-06
dc.identifier.citation	APPLIED SCIENCES-BASEL, 2023, 13, (12)
dc.identifier.issn	2076-3417
dc.identifier.issn	2076-3417
dc.identifier.uri	http://hdl.handle.net/10453/177340
dc.description.abstract	<jats:p>In real-world scenarios, images may be affected by additional noise during compression and transmission, which interferes with postprocessing such as image segmentation and feature extraction. Image noise can also be induced by environmental variables and imperfections in the imaging equipment. Robust principal component analysis (RPCA), one of the traditional approaches for denoising images, suffers from a failure to efficiently use the background’s low-rank prior information, which lowers its effectiveness under complex noise backgrounds. In this paper, we propose a robust PCA method based on a nonconvex low-rank approximation and total variational regularization (TV) to model the image denoising problem in order to improve the denoising performance. Firstly, we use a nonconvex γ-norm to address the issue that the traditional nuclear norm penalizes large singular values excessively. The rank approximation is more accurate than the nuclear norm thanks to the elimination of matrix elements with substantial approximation errors to reduce the sparsity error. The method’s robustness is improved by utilizing the low sensitivity of the γ-norm to outliers. Secondly, we use the l1-norm to increase the sparsity of the foreground noise. The TV norm is used to improve the smoothness of the graph structure in accordance with the sparsity of the image in the gradient domain. The denoising effectiveness of the model is increased by employing the alternating direction multiplier strategy to locate the global optimal solution. It is important to note that our method does not require any labeled images, and its unsupervised denoising principle enables the generalization of the method to different scenarios for application. Our method can perform denoising experiments on images with different types of noise. Extensive experiments show that our method can fully preserve the edge structure information of the image, preserve important features of the image, and maintain excellent visual effects in terms of brightness smoothing.</jats:p>
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	APPLIED SCIENCES-BASEL
dc.relation.isbasedon	10.3390/app13127184
dc.rights	info:eu-repo/semantics/openAccess
dc.title	An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization
dc.type	Journal Article
utslib.citation.volume	13
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-28T04:15:35Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	12

Abstract:

In real-world scenarios, images may be affected by additional noise during compression and transmission, which interferes with postprocessing such as image segmentation and feature extraction. Image noise can also be induced by environmental variables and imperfections in the imaging equipment. Robust principal component analysis (RPCA), one of the traditional approaches for denoising images, suffers from a failure to efficiently use the background’s low-rank prior information, which lowers its effectiveness under complex noise backgrounds. In this paper, we propose a robust PCA method based on a nonconvex low-rank approximation and total variational regularization (TV) to model the image denoising problem in order to improve the denoising performance. Firstly, we use a nonconvex γ-norm to address the issue that the traditional nuclear norm penalizes large singular values excessively. The rank approximation is more accurate than the nuclear norm thanks to the elimination of matrix elements with substantial approximation errors to reduce the sparsity error. The method’s robustness is improved by utilizing the low sensitivity of the γ-norm to outliers. Secondly, we use the l1-norm to increase the sparsity of the foreground noise. The TV norm is used to improve the smoothness of the graph structure in accordance with the sparsity of the image in the gradient domain. The denoising effectiveness of the model is increased by employing the alternating direction multiplier strategy to locate the global optimal solution. It is important to note that our method does not require any labeled images, and its unsupervised denoising principle enables the generalization of the method to different scenarios for application. Our method can perform denoising experiments on images with different types of noise. Extensive experiments show that our method can fully preserve the edge structure information of the image, preserve important features of the image, and maintain excellent visual effects in terms of brightness smoothing.

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http://hdl.handle.net/10453/177340