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Sparse representation for restoring images by exploiting topological structure of graph of patches

Yaxian Gao, Zhaoyuan Cai, Xianghua Xie Orcid Logo, Jingjing Deng, Zengfa Dou Orcid Logo, Xiaoke Ma Orcid Logo

IET Image Processing, Volume: 19, Issue: 1

Swansea University Author: Xianghua Xie Orcid Logo

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DOI (Published version): 10.1049/ipr2.70004

Abstract

Image restoration poses a significant challenge, aiming to accurately recover damaged images by delving into their inherent characteristics. Various models and algorithms have been explored by researchers to address different types of image distortions, including sparse representation, grouped spars...

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Published in: IET Image Processing
ISSN: 1751-9659 1751-9667
Published: Institution of Engineering and Technology (IET) 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa68733
first_indexed 2025-01-24T09:39:07Z
last_indexed 2025-03-04T05:37:48Z
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spelling 2025-03-03T14:24:13.8092329 v2 68733 2025-01-24 Sparse representation for restoring images by exploiting topological structure of graph of patches b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2025-01-24 MACS Image restoration poses a significant challenge, aiming to accurately recover damaged images by delving into their inherent characteristics. Various models and algorithms have been explored by researchers to address different types of image distortions, including sparse representation, grouped sparse representation, and low-rank self-representation. The grouped sparse representation algorithm leverages the prior knowledge of non-local self-similarity and imposes sparsity constraints to maintaintexture information within images. To further exploit the intrinsic properties of images, this study proposes a novel low-rank- representation-guided grouped sparse representation image restoration algorithm. This algorithm integrates self-representation models and trace optimization techniques to effectively preserve the original image structure, thereby enhancing image restoration performance while retaining the original texture and structural information. We evaluate the proposed method on image denoising and deblocking tasks across several datasets, demonstrating promising results. Journal Article IET Image Processing 19 1 Institution of Engineering and Technology (IET) 1751-9659 1751-9667 image representation; image restoration 24 1 2025 2025-01-24 10.1049/ipr2.70004 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Another institution paid the OA fee Shaanxi Key Research and Development Program. Grant Number: 2021ZDLGY02-02 2025-03-03T14:24:13.8092329 2025-01-24T09:36:04.4280671 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Yaxian Gao 1 Zhaoyuan Cai 2 Xianghua Xie 0000-0002-2701-8660 3 Jingjing Deng 4 Zengfa Dou 0000-0002-5162-6244 5 Xiaoke Ma 0000-0002-5604-7137 6 68733__33727__b5cf3a7452844a919ef0a558e4729829.pdf 68733.VoR.pdf 2025-03-03T14:07:56.9269659 Output 3650375 application/pdf Version of Record true © 2025 The Author(s). This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License. true eng http://creativecommons.org/licenses/by-nc/4.0/
title Sparse representation for restoring images by exploiting topological structure of graph of patches
spellingShingle Sparse representation for restoring images by exploiting topological structure of graph of patches
Xianghua Xie
title_short Sparse representation for restoring images by exploiting topological structure of graph of patches
title_full Sparse representation for restoring images by exploiting topological structure of graph of patches
title_fullStr Sparse representation for restoring images by exploiting topological structure of graph of patches
title_full_unstemmed Sparse representation for restoring images by exploiting topological structure of graph of patches
title_sort Sparse representation for restoring images by exploiting topological structure of graph of patches
author_id_str_mv b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
author Xianghua Xie
author2 Yaxian Gao
Zhaoyuan Cai
Xianghua Xie
Jingjing Deng
Zengfa Dou
Xiaoke Ma
format Journal article
container_title IET Image Processing
container_volume 19
container_issue 1
publishDate 2025
institution Swansea University
issn 1751-9659
1751-9667
doi_str_mv 10.1049/ipr2.70004
publisher Institution of Engineering and Technology (IET)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description Image restoration poses a significant challenge, aiming to accurately recover damaged images by delving into their inherent characteristics. Various models and algorithms have been explored by researchers to address different types of image distortions, including sparse representation, grouped sparse representation, and low-rank self-representation. The grouped sparse representation algorithm leverages the prior knowledge of non-local self-similarity and imposes sparsity constraints to maintaintexture information within images. To further exploit the intrinsic properties of images, this study proposes a novel low-rank- representation-guided grouped sparse representation image restoration algorithm. This algorithm integrates self-representation models and trace optimization techniques to effectively preserve the original image structure, thereby enhancing image restoration performance while retaining the original texture and structural information. We evaluate the proposed method on image denoising and deblocking tasks across several datasets, demonstrating promising results.
published_date 2025-01-24T12:39:26Z
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score 11.059316

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