Abstract
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 e
Highlights
Vision is one of the important channels for human beings to obtain information
By studying the limitations of the NCSR algorithm, this paper proposes that based on non-local clustering of the image denoising algorithm for sparse priors.The algorithm matches from the block both strategies and sparse prior sources are studied in order to achieve better the denoising performance
This paper studies the limitations of NCSR algorithm
Summary
Vision is one of the important channels for human beings to obtain information. Image is an important medium for transmitting visual information. Image denoising is an important and meaningful thing [1,2,3]. Strong noise images because of strong noise disruption, SNR is very low. The human eye can only see the rough outline of the object, and the details cannot be identified. Because of the presence of strong noise, it is more difficult to denoise the strong noise images [4,5]. Most of the image denoising algorithms do not denoise for strong noise, and many denoising methods have poor results when faced with strong noise
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