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Anisotropic Non-Local Means with Spatially Adaptive Patch Shapes

  • Charles-Alban Deledalle
  • Vincent Duval
  • Joseph Salmon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6667)

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.

Keywords

Image denoising non-local means spatial adaptivity aggregation risk estimation SURE 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Charles-Alban Deledalle
    • 1
  • Vincent Duval
    • 1
  • Joseph Salmon
    • 2
  1. 1.Institut Telecom – Telecom ParisTech – CNRS LTCIParisFrance
  2. 2.Université Paris 7 – Diderot– LPMA – CNRS-UMR 7599ParisFrance

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