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A Content-Adaptive Method for Image Denoising
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DOI：10.1109/ACCESS.2019.2958697.png)
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摘要
En 中文
In this paper, we firstly analyze the statistical distribution of simultaneous sparse coding errors (SSCE), which reflects the local correlation and non-local correlation characteristics of natural images. Based on the observation, we establish the optimal denoising problem which uses L-1 norm to constrain the image prior. According to the close-form solution of the proposed problem, we find that the denoising limit is only determined by the patch number, patch size and the variances of different bands in SSCE. Then we exploit the relationships between the patch complexity, patch size, patch number and the denoising limit. The study shows that a content-adaptive strategy may be useful to obtain better denoising performance. We then design a content-adaptive method for image denoising in which the groups are adaptively determined according to the structural complexities of reference patches. Experimental results show that the proposed scheme achieves competitive performance with several state-of-the-art methods in both subjective and objective aspects.
Keyword:
Image denoising
structural complexity
patch size
patch number
denoising limit
content-adaptive
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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