Anisotropic non-local means with spatially adaptive patch shapes

Authors:
Charles-Alban Deledalle;Vincent Duval;Joseph Salmon
Affiliations:
Institut Telecom --- Telecom ParisTech --- CNRS LTCI, Paris, France;Institut Telecom --- Telecom ParisTech --- CNRS LTCI, Paris, France;Université Paris 7 --- Diderot--- LPMA --- CNRS-UMR 7599, Paris, France
Venue:
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Year:
2011

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Abstract

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.