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Poisson NL-means

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Description of the filter

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Qualitative Evaluation of the Denoising Algorithms

lena: poisson_noisy
lena: poisson_tv
lena: poisson_nlm

 
table: poisson_noisy
table: poisson_tv
table: poisson_nlm

 
mito: poisson_noisy(a)
mito: poisson_tv(b)
mito: poissson_nlm(c)
(a) Original images damaged by Poisson noise, denoised images obtained by (b) Poisson TV [3] and (c) the proposed Poisson NL means.

Image courtesy of Y. Tourneur for the image of a mitochondrion (Tetramethylrhodamine methyl ester, TMRM) sensed in low-light conditions by confocal fluorescence microscopy.

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Quantitative Evaluation of the Denoising Algorithms

TABLE
SNR values of estimated images using different methods on images damaged by Poisson noise with different levels of degradation. The optimal parameters and the number of iterations of the proposed Poisson NL means are given.
Barbara (256 x 256)
Noisy 1.13 4.08 9.31 14.04
MA filter 7.50 7.75 7.89 7.94
Poisson TV [3] 8.07 9.16 9.39 9.45
NL means [1] 9.43 11.54 14.64 16.92
Poisson NL means 10.31 12.16 14.84 17.01
Boat (256 x 256)
Noisy -0.76 2.28 7.45 12.29
MA filter 6.85 7.11 7.30 7.35
Poisson TV [3] 8.40 9.39 9.60 9.61
NL means [1] 7.92 9.58 12.28 14.28
Poisson NL means 9.03 10.42 12.59 14.36
House (256 x 256)
Noisy -1.18 1.83 7.07 11.88
MA filter 10.20 10.87 11.34 11.57
Poisson TV [3] 10.87 13.46 13.96 14.21
NL means [1] 10.67 13.37 16.97 19.63
Poisson NL means 13.18 14.91 17.70 19.99
Lena (256 x 256)
Noisy -0.34 2.68 7.89 12.64
MA filter 8.69 9.07 9.38 9.45
Poisson TV [3] 9.53 11.68 12.23 12.30
NL means [1] 9.81 11.65 14.68 17.05
Poisson NL means 11.46 12.86 15.48 17.44

 
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Poisson NL-means software

Download the Poisson NL means filter

These pieces of Matlab softwares are based on C++ Mex-Functions compiled for Linux 32-bit, Linux 64-bit and Windows 32 bit. Matlab script exemples are given, they have been written for MATLAB with the Image Processing Toolbox (to load the images) and the Statistic Toolbox (to generate Poisson noise). Please refer to the REAME file for more details. For any comment, suggestion or question please contact Charles-Alban Deledalle at deledalle (at) telecom-paristech (dot) fr.

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References

  1. Buades, A. and Coll, B. and Morel, J.M.
    A Non-Local Algorithm for Image Denoising,
    IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 2005
  2. Charles-Alban Deledalle, Loïc Denis and Florence Tupin,
    Iterative Weighted Maximum Likelihood Denoising with Probabilistic Patch-Based Weights,
    IEEE Trans. on Image Processing, vol. 18, no. 12, pp. 2661-2672, December 2009 (download)
  3. T. Le, R. Chartrand, and T. Asaki,
    A variational approach to reconstructing images corrupted by Poisson noise,
    J. of Math. Imaging and Vision, vol. 27, no. 3, pp. 257-263, 2007.
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In France (currently out of this office):
Charles Deledalle - charles-alban (dot) deledalle (at) math.u-bordeaux (dot) fr
Bureau 209
Institut de Mathématiques de Bordeaux
Université Bordeaux
351, cours de la Libération - F-33405 TALENCE cedex
FRANCE
+33 (0)5 40 00 21 14
 
In USA (currently at this office):
Charles Deledalle - cdeledalle (at) ucsd (dot) edu
Jacobs Hall, Room 4808
Jacobs School of Engineering
University of California, San Diego
9500 Gilman Drive
La Jolla, CA 92093
USA
 
Last modified: Wed Aug 28 11:22:39 Europe/Berlin 2013