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Fast Non Local Means Denoising for 3D MR Images

  • Pierrick Coupé
  • Pierre Yger
  • Christian Barillot
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)

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.

Keywords

Ground Truth Noise Removal Search Volume Denoising Method Denoising Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Pierrick Coupé
    • 1
  • Pierre Yger
    • 1
    • 2
  • Christian Barillot
    • 1
  1. 1.Unit/Project VisAGeS U746, INSERM – INRIA – CNRS – Univ-Rennes 1, IRISARennesFrance
  2. 2.Brittany Extension – CS/IT DepartmentENS CachanBruzFrance

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