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Single image denoising via multi-scale weighted group sparse coding
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AI
DOI：10.1016/j.sigpro.2022.108650.png)
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Abstract
En 中文
Nonlocal self-similarity (NSS) property of natural images, which means that the structure of the image sub-patches will appear repeatedly within a certain area, has been widely exploited as an effective prior to establishing various models in image denoising task. However, most of the existing NSS-based de noising models exploit the NSS prior in single scale only, and for some of the image patches that do not appear repeatedly, undesirable ringing artifacts will occur in the restored image, and even the image content may be lost. Considering the fact that NSS exists both within the same scale and across different scales, in order to better restore the structure and the edges of images contaminated by noise, we propose, in this paper, a novel multi-scale weighted group sparse coding model (MS-WGSC) for image denoising, wherein the patch groups are constructed using multi-scale NSS priors. Furthermore, an alternating minimization method is proposed to obtain the solution for our model. Extensive experiments are conducted that demonstrate the competitiveness of the proposed model compared with that of stateof-the-art methods not only in terms of the quantitative metrics such as PSNR and SSIM, but also in perceptual quality.(c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Model-driven method
multi-scale self-similarity
weighted group sparse coding
image denoising
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