sis 22(4): 14

Research Article

Non-local clustering via sparse prior for sports image denoising

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  • @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
Ying Zhang1,*
  • 1: Henan Institute of Technology
*Contact email: 352720214@qq.com

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This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173794.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.