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Image restoration with group sparse representation and low‐rank group residual learning
,
Jingjing Deng,
Zengfa Dou ,
Bo Tong,
Xiaoke Ma
IET Image Processing, Volume: 18, Issue: 3, Pages: 741 - 760
Swansea University Author:
Xianghua Xie
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© 2023 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
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DOI (Published version): 10.1049/ipr2.12982
Abstract
Image restoration, as a fundamental research topic of image processing, is to reconstruct the original image from degraded signal using the prior knowledge of image. Group sparse representation (GSR) is powerful for image restoration; it however often leads to undesirable sparse solutions in practic...
| Published in: | IET Image Processing |
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| ISSN: | 1751-9659 1751-9667 |
| Published: |
Wiley
2024
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa64966 |
| Abstract: |
Image restoration, as a fundamental research topic of image processing, is to reconstruct the original image from degraded signal using the prior knowledge of image. Group sparse representation (GSR) is powerful for image restoration; it however often leads to undesirable sparse solutions in practice. In order to improve the quality of image restoration based on GSR, the sparsity residual model expects the representation learned from degraded images to be as close as possible to the true representation. In this article, a group residual learning based on low-rank self-representation is proposed to automatically estimate the true group sparse representation. It makes full use of the relation among patches and explores the subgroup structures within the same group, which makes the sparse residual model have better interpretation furthermore, results in high-quality restored images. Extensive experimental results on two typical image restoration tasks (image denoising and deblocking) demonstrate that the proposed algorithm outperforms many other popular or state-of-the-art image restoration methods. |
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| Keywords: |
group residual learning, group sparse representation, image restoration, low-rank self-representation |
| College: |
Faculty of Science and Engineering |
| Funders: |
This work was supported by the Shaanxi Key Research and Development Program (Program No. 2021ZDLGY02-02). |
| Issue: |
3 |
| Start Page: |
741 |
| End Page: |
760 |