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Image restoration with group sparse representation and low‐rank group residual learning

Zhaoyuan Cai, Xianghua Xie Orcid Logo, Jingjing Deng, Zengfa Dou Orcid Logo, Bo Tong, Xiaoke Ma Orcid Logo

IET Image Processing

Swansea University Author: Xianghua Xie Orcid Logo

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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...

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Published in: IET Image Processing
ISSN: 1751-9659 1751-9667
Published: Institution of Engineering and Technology (IET)
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa64966
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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.
College: College of Science