A Content-Adaptive Method for Image Denoising
A Content-Adaptive Method for Image Denoising
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DOI：
10.1109/access.2019.2958697
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发表时间：
2019
期刊：
影响因子：
3.9
通讯作者：
Hao Chen;Yi He;Ling Wei;Xiqi Li;Jinsheng Yang;Yudong Zhang
中科院分区：
文献类型：
--
作者：
Hao Chen;Yi He;Ling Wei;Xiqi Li;Jinsheng Yang;Yudong Zhang
本文首先分析了同时稀疏编码误差（SSCE）的统计分布，它反映了自然图像的局部相关和非局部相关特性。在此基础上，建立了利用L1范数约束图像先验的最优去噪问题。根据所提出的问题的封闭解，我们发现去噪极限只取决于补丁的数量，补丁的大小和不同波段的方差在SSCE。然后，我们利用补丁的复杂性，补丁大小，补丁数量和去噪极限之间的关系。研究表明，内容自适应策略可能有助于获得更好的去噪性能。然后，我们设计了一个内容自适应的方法，在图像去噪中的组是自适应地确定根据结构的复杂性的参考补丁。实验结果表明，该方案在主观和客观方面都取得了与现有方法相媲美的性能。
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.