Fast Non Local Means Denoising for 3D MR Images

Pierrick Coupé 1 Pierre Yger 1 Christian Barillot 1
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : One critical issue in the context of image restoration is the problem of noise removal while keeping the integrity of relevant image information. Denoising is a crucial step to increase image conspicuity and to improve the performances of all the processings needed for quantitative imaging analysis. The method proposed in this paper is based on an optimized version of the Non Local (NL) Means algorithm. This approach uses the natural redundancy of information in image to remove the noise. Tests were carried out on synthetic datasets and on real 3T MR images. The results show that the NL-means approach outperforms other classical denoising methods, such as Anisotropic Diffusion Filter and Total Variation.
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https://hal.inria.fr/inria-00131287
Contributor : Pierrick Coupé <>
Submitted on : Tuesday, February 27, 2007 - 11:13:52 AM
Last modification on : Monday, March 4, 2019 - 2:07:40 PM
Long-term archiving on : Tuesday, September 21, 2010 - 12:06:20 PM

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Pierrick Coupé, Pierre Yger, Christian Barillot. Fast Non Local Means Denoising for 3D MR Images. 9th International Conference on Medical Image Computing and Computer-Assisted Intervention, Oct 2006, Copenhagen, Denmark, pp.33-40, ⟨10.1007/11866763_5⟩. ⟨inria-00131287v2⟩

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