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Edge Preserving Probabilistic Smoothing Algorithm

  • Bogdan Smolka
  • Konrad W. Wojciechowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1689)

Abstract

In the presented paper a new probabilistic approach to the problem of noise reduction has been presented. It is based on the concept of a virtual particle performing a random walk on the image lattice, with transition probabilities derived from the median distribution. The probabilistic smoothing algorithm combined with a cooling procedure, known from the simulated annealing optimization method, constitutes a new powerful technique of noise suppression, capable of preserving edges and other image features.

Keywords

Noise Reduction Image Lattice Noisy Image Impulse Noise Noise Suppression 
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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References

  1. 1.
    Nagao M. Matsuyama T.: Edge preserving smoothing, Computer Graphics & Image Processing, 9, 394–407, 1979CrossRefGoogle Scholar
  2. 2.
    Klette R. Zamperoni P.: Handbuch der Operatoren für die Bildverarbeitung, Vieweg Verlag, Braunschweig, Wiesbaden, 1992Google Scholar
  3. 3.
    Zamperoni P.: Methoden der digitalen Bildsignalverarbeitung, Vieweg, Braunschweig, 1991Google Scholar
  4. 4.
    Yaroslavsky L. Murray E.: Fundamentals of Digital Optics, Birkhäuser, Boston, 1996zbMATHGoogle Scholar
  5. 5.
    Pratt W.: Digital Image Processing, New York, John Willey & Sons 1991zbMATHGoogle Scholar
  6. 6.
    Gonzalez R.C. Woods R.E.: Digital Image Processing, Reading MA, Addison-Wesley, 1992Google Scholar
  7. 7.
    Scher A. Dias V. Rosenfeld F.R.: Some new image smoothing techniques, IEEE Trans. on SMC, 10, 153–158, March 1980Google Scholar
  8. 8.
    Wang D. Vagnucci A.H. Li C.C.: Gradient inverse smoothing scheme and the evaluation of its performance, Computer Graphics & Image Processing, 15, 167–181, 1981CrossRefGoogle Scholar
  9. 9.
    Lee J.S.: Digital image enhancement and noise filtering by use of local statistics, PAMI 2, 165–168, 1980Google Scholar
  10. 10.
    Wang D. Wang Q.: A weighted averaging method for image smoothing, Proceedings of the 8th. ICPR, 981–983, Paris, 1988Google Scholar
  11. 11.
    Smith S.M. Brady J.M.: SUSAN — a new approach to low level image processing, Int. Journal of Computer Vision, 23,1, 45–78, 1997CrossRefGoogle Scholar
  12. 12.
    Lee J.S.: Digital image smoothing and the sigmafilter, Computer Vision Graphics and Image Processing, 24, 255–269, 1983CrossRefGoogle Scholar
  13. 13.
    Lohmann G.: Volumetric Image Analysis, John Wiley and Teubner, 1988Google Scholar
  14. 14.
    Spitzer F.: Principles of Random Walk, D. van Nostrand Company, Princeton, New Jersey, 1975Google Scholar
  15. 15.
    van Laarhoven P.J.M. Aarts E.A.: Simulated Annealing: Theory and Application, Kluver Academic Publishers, Dordrecht, 1989Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Bogdan Smolka
    • 1
  • Konrad W. Wojciechowski
    • 1
  1. 1.Dept. of Automatics Electronics and Computer ScienceSilesian University of TechnologyGliwicePoland

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