University of Oulu

Zhiyuan Z., Xinggan Z., Qiong W., Yechao B., Yang C., Lan T., Xin L. Group sparsity residual constraint for image denoising with external nonlocal self-similarity prior. Neurocomputing, Volume 275, 2018, Pages 2294-2306, ISSN 0925-2312. https://doi.org/10.1016/j.neucom.2017.11.004.

Group sparsity residual constraint for image denoising with external nonlocal self-similarity prior

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Author: Zha, Zhiyuan1; Zhang, Xinggan1; Wang, Qiong1;
Organizations: 1School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China
2National Mobile Communication Research Lab., Southeast University, Nanjing 210023, China
3The Center for Machine Vision and Signal Analysis, University of Oulu, 90014, Finland
Format: article
Version: accepted version
Access: embargoed
Persistent link: http://urn.fi/urn:nbn:fi-fe201902215927
Language: English
Published: Elsevier, 2018
Publish Date: 2019-11-09
Description:

Abstract

Nonlocal image representation has been successfully used in many image-related inverse problems including denoising, deblurring and deblocking. However, most existing methods only consider the nonlocal self-similarity (NSS) prior of degraded observation image, and few methods use the NSS prior from natural images. In this paper we propose a novel method for image denoising via group sparsity residual constraint with external NSS prior (GSRC-ENSS). Different from the previous NSS prior-based denoising methods, two kinds of NSS prior (e.g., NSS priors of noisy image and natural images) are used for image denoising. In particular, to enhance the performance of image denoising, the group sparsity residual is proposed, and thus the problem of image denoising is translated into reducing the group sparsity residual. Because the groups contain a large amount of NSS information of natural images, to reduce the group sparsity residual, we obtain a good estimation of the group sparse coefficients of the original image by the external NSS prior based on Gaussian Mixture Model (GMM) learning, and the group sparse coefficients of noisy image are used to approximate the estimation. To combine these two NSS priors better, an effective iterative shrinkage algorithm is developed to solve the proposed GSRC-ENSS model. Experimental results demonstrate that the proposed GSRC-ENSS not only outperforms several state-of-the-art methods, but also delivers the best qualitative denoising results with finer details and less ringing artifacts.

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Series: Neurocomputing
ISSN: 0925-2312
ISSN-E: 1872-8286
ISSN-L: 0925-2312
Volume: 275
Pages: 2294 - 2306
DOI: 10.1016/j.neucom.2017.11.004
OADOI: https://oadoi.org/10.1016/j.neucom.2017.11.004
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Copyright information: © 2017 Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
  https://creativecommons.org/licenses/by-nc-nd/4.0/