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Modified Ranked Order Adaptive Median Filter for Impulse Noise Removal

  • Anna Fabijańska
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)

Abstract

In this paper problem of impulse noise removal is considered. Specifically, modifications of ranked order adaptive median filter (RAMF) are proposed. RAMF is popular, well established and effective switching median filter for denoising images corrupted by impulse noise. However, the modifications proposed in this paper significantly improve its results, especially in case of highly corrupted images. Results of denosing of images under a wide range of noise corruption (5-95%) using the original and the modified ranked order median filter are presented, compared and discussed. Comparison is made by means of PSNR and SSIM index.

Keywords

Impulse Noise Noise Removal Noise Detection Noise Density Noisy Pixel 
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.
    Arce, G.R., Gallagher, N.C., Nodes, T.: Median filters: theory and applications. In: Huang, T. (ed.) Advances in Computer Vision and Image Processing. JAI Press, CT (1986)Google Scholar
  2. 2.
    Astola, J., Kuosmanen, P.: Fundamentals of nonlinear digital filtering. CRC Press, USA (1997)Google Scholar
  3. 3.
    Chen, T., Wu, H.R.: Adaptive impulse detection using center-weighted median filters. IEEE Signal Processing Letters 8(1), 1–3 (2001)CrossRefGoogle Scholar
  4. 4.
    Gil, J., Werman, M.: Computing 2-D min, median, and max filters. IEEE Trans. Pattern Analysis and Machine Intelligence 15(5), 504–507 (1993)CrossRefGoogle Scholar
  5. 5.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice Hall, USA (2007)Google Scholar
  6. 6.
    Guangjin, Z., Jiegu, L.: Some problems of 2D morphological and median filters. Journal of Shanghai University (English Edition) 1(3), 245–248 (1997)CrossRefGoogle Scholar
  7. 7.
    Hwang, H., Haddad, R.A.: Adaptive median filters: New algorithms and results. IEEE Trans. Image Processing 4(4), 499–502 (1995)CrossRefGoogle Scholar
  8. 8.
    Lin, H.M., Wilson, A.N.: Median filters with adaptive length. IEEE Trans. Circuits and Systems 35(6), 675–690 (1988)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Ng, P.-E., Ma, K.-K.: A switching median filter with boundary discriminative noise detection for extremely corrupted images. IEEE Trans. Image Processing 15(6), 1506–1516 (2006)CrossRefGoogle Scholar
  10. 10.
    Pitas, I., Venetsanopoulos, A.N.: Order statistics in digital image processing. Proc. IEEE 80(12), 1893–1921 (1992)CrossRefGoogle Scholar
  11. 11.
    Vijaykumar, V.R., Vanathi, P.T., Kanagasabapathy, P., Ebenezer, D.: High density impulse noise removal using robust estimation based filter. IAENG International Journal of Computer Science 35(3) (2008), http://www.iaeng.org/IJCS/issues_v35/issue_3/index.html (accessed November 2010)
  12. 12.
    Wang, Z., Bovik, A.C.: Mean squared error: love it or leave it. IEEE Signal Proc. Magazine 26(1), 98–117 (2009)CrossRefGoogle Scholar
  13. 13.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Processing 13(4), 600–612 (2004)CrossRefGoogle Scholar
  14. 14.
    Yin, L., Yang, R., Gabbouj, M., Neuvo, Y.: Weighted median filters: a tutorial. IEEE Trans. Circuits and Systems 43(3), 157–192 (1996)CrossRefGoogle Scholar
  15. 15.
    Zvonarev, P.S., Apalkov, I.V., Khryashchev, V.V., Reznikova, I.V.: Neural network adaptive switching median filter for the restoration of impulse noise corrupted images. In: Kamel, M.S., Campilho, A.C. (eds.) ICIAR 2005. LNCS, vol. 3656, pp. 223–230. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Anna Fabijańska
    • 1
  1. 1.Computer Engineering DepartmentTechnical University of LodzLodzPoland

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