Fast non local means denoising for 3d MR images

Authors:
Pierrick Coupé;Pierre Yger;Christian Barillot
Affiliations:
Unit/Project VisAGeS U746, INSERM – INRIA – CNRS – Univ-Rennes 1, IRISA, Rennes, France;Unit/Project VisAGeS U746, INSERM – INRIA – CNRS – Univ-Rennes 1, IRISA, Rennes, France;Unit/Project VisAGeS U746, INSERM – INRIA – CNRS – Univ-Rennes 1, IRISA, Rennes, France
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2006

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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.