Journal article 12 views
Sparse representation for restoring images by exploiting topological structure of graph of patches
Yaxian Gao,
Zhaoyuan Cai,
Xianghua Xie 
,
Jingjing Deng,
Zengfa Dou,
Xiaoke Ma
,
Jingjing Deng,
Zengfa Dou,
Xiaoke Ma
IET Image Processing
Swansea University Author:
Xianghua Xie
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...
| Published in: | IET Image Processing |
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| Published: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa68733 |
| 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 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. |
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| College: |
Faculty of Science and Engineering |