Anisotropic Non-Local Means with Spatially Adaptive Patch Shapes

被引:0
作者
Deledalle, Charles-Alban [1 ]
Duval, Vincent [1 ]
Salmon, Joseph [2 ]
机构
[1] CNRS LTCI, Telecom ParisTech, Inst Telecom, 46 Rue Barrault, F-75634 Paris 13, France
[2] Univ Paris, LPMA, CNRS, UMR, Paris, France
来源
SCALE SPACE AND VARIATIONAL METHODS IN COMPUTER VISION | 2012年 / 6667卷
关键词
Image denoising; non-local means; spatial adaptivity; aggregation; risk estimation. SURE; IMAGE; SURE; SMOOTHNESS; ADAPTATION; PARAMETERS; ALGORITHM;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper is about extending the classical Non-Local Means (NLM) denoising algorithm using general shapes instead of square patches. The use of various shapes enables to adapt to the local geometry of the image while looking for pattern redundancies. A fast FFT-based algorithm is proposed to compute the NLM with arbitrary shapes. The local combination of the different shapes relies on Stein's Unbiased Risk Estimate (SURE). To improve the robustness of this local aggregation, we perform an anistropic diffusion of the risk estimate using a properly modified Perona-Malik equation. Experimental results show that this algorithm improves the NLM performance and it removes some visual artifacts usually observed with the NLM.
引用
收藏
页码:231 / +
页数:3
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