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A Filter To Remove Gaussian Noise by Clustering the Gray Scale

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Abstract

An algorithm to suppress Gaussian noise is presented, based on clustering (grouping) gray levels. The histogram of a window sliding across the image is divided into clusters, and the algorithm outputs the mean level of the group containing the central pixel of the window. This filter restores well the majority of noisy pixels, leaving only few of them very deviated, that can be finally restored with a common filter for impulsive noise, such as a median filter. In this paper the clustering filter CF is described, analysed and compared with other similar filters.

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References

  1. L. Alvarez, J. Esclarín, “Image Quantization by Nonlinear Smoothing”, SPIE, Vol. 2567, No. 18, pp. 182-192, 1995.

    Google Scholar 

  2. M.R. Anderberg, R.K. Blashfield, “Cluster Analysis”, Sage Pub. Inc., Beverly Hills, 1984.

    Google Scholar 

  3. D.R.K. Brownrigg, “Weighted Median Filters”, Commun. Assoc. Comput. Machinery, Vol. 27, pp. 807-818, August 1984.

  4. H.A. David, “Order Statistics”, John Wiley, 1981.

  5. R.C. Dubes, A.K. Jain, “Clustering Techniques: The User's Dilemma”, Pattern Recognition, Vol. 8, pp. 247-260, 1976.

    Google Scholar 

  6. F. Godtliebsen, E. Spjøtvoll, “Comparison of Statistical Methods in MR Imaging”, Int. J. Imaging Systems and Technology, Vol. 3, pp. 33-39, 1991.

    Google Scholar 

  7. R.M. Haralick, L.G. Shapiro, “Image Segmentation Techniques”, Computer Vision, Graphics, and Image Processing, Vol. 29, pp. 100-132, 1985.

    Google Scholar 

  8. A.K Jain, R.C. Dubes, “Algorithms for Clustering Data”, Prentice-Hall, Englewood Cliffs, NJ, 1988.

    Google Scholar 

  9. B. Jäne, “Digital Image Processing”, Springer-Verlag, New York, 1995.

    Google Scholar 

  10. J.S. Lee, “Digital Image Smoothing and the Sigma Filter”, Computer Vision, Graphics, and Image Processing, Vol. 24, pp. 255-269, 1983.

    Google Scholar 

  11. R.S. Michlaski, R.F. Stepp, “Automated Construction of Classification: Conceptual Clustering Versus Numerical Taxonomy”, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 5, pp. 396-410, 1983.

    Google Scholar 

  12. I. Pitas, A.N. Venetsanopoulos, “Nonlinear Digital Filters: Principles and Applications”, Kluwer Academic Publisher, 1990.

  13. S. Theodoridis, K. Koutroumbas, “Pattern Recognition”, Academic Press, New York, 1999.

    Google Scholar 

  14. J. Weule, “Iteration Nichtlinearer Gauβ-Filter in der Bildverarbeitung”, Ph.D. Thesis, Heinrich-Heine-Universität Düsseldorf, 1994.

    Google Scholar 

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Atae-Allah, Z., Aroza, J.M. A Filter To Remove Gaussian Noise by Clustering the Gray Scale. Journal of Mathematical Imaging and Vision 17, 15–25 (2002). https://doi.org/10.1023/A:1020718507843

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  • DOI: https://doi.org/10.1023/A:1020718507843

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