Non-Local Methods with Shape-Adaptive Patches (NLM-SAP)

Charles-Alban Deledalle (1) , Vincent Duval (1) , Joseph Salmon (2)

1 LTCI - Laboratoire Traitement et Communication de l'Information
2 LPMA - Laboratoire de Probabilités et Modèles Aléatoires

We propose in this paper an extension of the Non-Local Means (NL-Means) denoising algorithm. The idea is to replace the usual square patches used to compare pixel neighborhoods with various shapes that can take advantage of the local geometry of the image. We provide a fast algorithm to compute the NL-Means with arbitrary shapes thanks to the fast Fourier transform. We then consider local combinations of the estimators associated with various shapes by using Stein's Unbiased Risk Estimate (SURE). Experimental results show that this algorithm improve the standard NL-Means performance and is close to state-of-the-art methods, both in terms of visual quality and numerical results. Moreover, common visual artifacts usually observed by denoising with NL-Means are reduced or suppressed thanks to our approach.

Mots clés: Image denoising; non-local means; spatial adaptivity; aggregation; risk estimation; SURE

Domaines: Traitement des images [eess.IV]; Statistiques [math.ST]; Théorie [stat.TH]

Charles-Alban Deledalle, Vincent Duval, Joseph Salmon. Non-Local Methods with Shape-Adaptive Patches (NLM-SAP). Journal of Mathematical Imaging and Vision, 2012, 43 (2), pp.103-120. ⟨10.1007/s10851-011-0294-y⟩. ⟨hal-00536723⟩