Research Article
Non-local clustering via sparse prior for sports image denoising
@ARTICLE{10.4108/eai.13-1-2022.172817, author={Ying Zhang}, title={Non-local clustering via sparse prior for sports image denoising}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={9}, number={4}, publisher={EAI}, journal_a={SIS}, year={2022}, month={1}, keywords={Image denoising, non-local clustering, sparse representation}, doi={10.4108/eai.13-1-2022.172817} }- Ying Zhang
Year: 2022
Non-local clustering via sparse prior for sports image denoising
SIS
EAI
DOI: 10.4108/eai.13-1-2022.172817
Abstract
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173794.
Image denoising is very important in image preprocessing. In order to introduce the priori information of external clean image into the denoising process, a non-local clustering image denoising algorithm is proposed. A sparse representation dictionary is obtained by combining the image blocks of external clean image and internal noise image. The sparse coefficient estimation of ideal image is obtained by global similar block matching. Based on the class dictionary and the estimated sparse coefficient, a sparse reconstruction method based on compressed sensing technology is used to denoise the image. Experimental results show that compared with traditional image denoising methods, the proposed algorithm can significantly reduce the denoising block effect and preserve more details while transitioning more naturally in the flat area of the image.
Copyright © 2022 Ying Zhang et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
