3D wavelet subbands mixing for image denoising

  • Authors:
  • Pierrick Coupé;Pierre Hellier;Sylvain Prima;Charles Kervrann;Christian Barillot

  • Affiliations:
  • University of Rennes I, CNRS UMR, IRISA, Rennes, France and INRIA, IRISA, Rennes, France and IRISA, INSERM, Rennes, France;University of Rennes I, CNRS UMR, IRISA, Rennes, France and INRIA, IRISA, Rennes, France and IRISA, INSERM, Rennes, France;University of Rennes I, CNRS UMR, IRISA, Rennes, France and INRIA, IRISA, Rennes, France and IRISA, INSERM, Rennes, France;Mathematiques et Informatique Appliquées, INRA, France and VISTA Project-Team, IRISA, INRIA, Rennes, France;University of Rennes I, CNRS UMR, IRISA, Rennes, France and INRIA, IRISA, Rennes, France and IRISA, INSERM, Rennes, France

  • Venue:
  • Journal of Biomedical Imaging
  • Year:
  • 2008

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Abstract

A critical issue in image restoration is the problem of noise removal while keeping the integrity of relevant image information. The method proposed in this paper is a fully automatic 3D blockwise version of the nonlocal (NL) means filter with wavelet subbands mixing. The proposed wavelet subbands mixing is based on a multiresolution approach for improving the quality of image denoising filter. Quantitative validation was carried out on synthetic datasets generated with the Brain Web simulator. The results show that our NL-means filter with wavelet subbands mixing outperforms the classical implementation of the NL-means filter in terms of denoising quality and computation time. Comparison with wellestablished methods, such as nonlinear diffusion filter and total variation minimization, shows that the proposed NL-means filter produces better denoising results. Finally, qualitative results on real data are presented.