University of Oulu

Z. Zha et al., "Image denoising via group sparsity residual constraint," 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, 2017, pp. 1787-1791. doi: 10.1109/ICASSP.2017.7952464

Image denoising via group sparsity residual constraint

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Author: Zha, Zhiyuan1,2; Liu, Xin3; Zhou, Ziheng3;
Organizations: 1School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China
2School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
3The Center for Machine Vision and Signal Analysis, University of Oulu, 90014, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe201902226050
Language: English
Published: Institute of Electrical and Electronics Engineers, 2017
Publish Date: 2019-02-22
Description:

Abstract

Group sparsity has shown great potential in various low-level vision tasks (e.g, image denoising, deblurring and inpainting). In this paper, we propose a new prior model for image denoising via group sparsity residual constraint (GSRC). To enhance the performance of group sparse-based image denoising, the concept of group sparsity residual is proposed, and thus, the problem of image denoising is translated into one that reduces the group sparsity residual. To reduce the residual, we first obtain some good estimation of the group sparse coefficients of the original image by the first-pass estimation of noisy image, and then centralize the group sparse coefficients of noisy image to the estimation. Experimental results have demonstrated that the proposed method not only outperforms many state-of-the-art denoising methods such as BM3D and WNNM, but results in a faster speed.

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Series: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing
ISSN: 1520-6149
ISSN-E: 2379-190X
ISSN-L: 1520-6149
ISBN Print: 978-1-5090-4117-6
Pages: 1787 - 1791
DOI: 10.1109/ICASSP.2017.7952464
OADOI: https://oadoi.org/10.1109/ICASSP.2017.7952464
Host publication: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Conference: IEEE international conference on acoustics, speech and signal processing
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
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