Advertisement

Nonparametric Impulsive Noise Removal

  • Bogdan Smolka
  • Rastislav Lukac
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3211)

Abstract

In this paper a novel class of filters designed for the removal of impulsive noise in color images is presented. The proposed filter class is based on the nonparametric estimation of the density probability function in a sliding filter window. The obtained results show good noise removal capabilities and excellent structure preserving properties of the new impulsive noise removal technique.

Keywords

Central Pixel Impulsive Noise Optimal Bandwidth Minkowski Norm Color Image Processing 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Pitas, I., Tsakalides, P.: Multivariate ordering in color image processing. IEEE Trans. on Circuits and Systems for Video Technology 1(3), 247–256 (1991)CrossRefGoogle Scholar
  2. 2.
    Tang, K., Astola, J., Neuovo, Y.: Nonlinear multivariate image filtering techniques. IEEE Trans. on Image Processing 4(6), 788–797 (1995)CrossRefGoogle Scholar
  3. 3.
    Trahanias, P.E., Venetsanopoulos, A.N.: Vector directional filters: a new class of multichannel image processing filters. IEEE Trans. on Image Processing 2(4), 528–534 (1993)CrossRefGoogle Scholar
  4. 4.
    Astola, J., Haavisto, P., Neuvo, Y.: Vector median filters. Proceedings of the IEEE 78, 678–689 (1990)CrossRefGoogle Scholar
  5. 5.
    Plataniotis, K.N., Venetsanopoulos, A.N.: Color Image Processing and Applications. Springer, Heidelberg (2000)Google Scholar
  6. 6.
    Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, London (1986)zbMATHGoogle Scholar
  7. 7.
    Scott, D.W.: Multivariate Density Estimation. John Wiley, New York (1992)zbMATHCrossRefGoogle Scholar
  8. 8.
    Kraaijveld, M.A.: A Parzen classifier with an improved robustness against deviations between training and test data. Pattern Recognition Letters 17, 679–689 (1996)CrossRefGoogle Scholar
  9. 9.
    Smolka, B., Plataniotis, K.N., Chydzinski, A., Szczepanski, M., Venetsanopulos, A.N., Wojciechowski, K.: Self-adaptive algorithm of impulsive noise reduction in color images. Pattern Recognition 35, 1771–1784 (2002)zbMATHCrossRefGoogle Scholar
  10. 10.
    Smolka, B., Lukac, R., Chydzinski, A., Plataniotis, K.N., Wojciechowski, K.: Fast adaptive similarity based impulsive noise reduction filter. Real Time Imaging 9, 261–276 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Bogdan Smolka
    • 1
  • Rastislav Lukac
    • 2
  1. 1.Polish-Japanese Institute of Information TechnologyWarsawPoland
  2. 2.The Edward S. Rogers Sr. Department of Electrical and Computer EngineeringUniversity of TorontoTorontoCanada

Personalised recommendations