Peer Group Vector Median Filter

  • Bogdan Smolka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4713)


In this paper, the properties of a novel color image filtering technique capable of impulse noise removal and edge enhancement are analyzed. The new filtering design is a generalization of the well known Vector Median Filter. The proposed filtering class is minimizing the cumulated dissimilarity measure of a group of pixels from the filtering window. The described filter is computationally efficient, easy to implement and very effective in suppressing impulsive noise, while preserving image details and strongly enhancing its edges.


Color Image Test Image Impulse Noise Mean Absolute Error Impulsive Noise 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Plataniotis, K.N., Venetsanopoulos, A.N.: Color Image Processing and Applications. Springer, Heidelberg (2000)Google Scholar
  2. 2.
    Lukac, R., Smolka, B., Martin, K., Plataniotis, K.N., Venetsanopoulos, A.N.: Vector filtering for color imaging. IEEE Signal Processing Magazine, Special Issue on Color Image Processing 22, 74–86 (2005)Google Scholar
  3. 3.
    Smolka, B., Plataniotis, K.N., Venetsanopoulos, A.N.: Nonlinear techniques for color image processing. In: Barner, K.E., Arce, G.R. (eds.) Nonlinear Signal and Image Processing: Theory, Methods, and Applications, pp. 445–505. CRC Press, Boca Raton, FL, USA (2004)Google Scholar
  4. 4.
    Smolka, B., Venetsanopoulos, A.N.: Noise reduction and edge detection in color images. In: Lukac, R., Plataniotis, K.N. (eds.) Color Image Processing: Methods and Applications, pp. 75–100. CRC Press, Boca Raton (2006)Google Scholar
  5. 5.
    Boncelet, C.G.: Image noise models. In: Bovik, A. (ed.) Handbook of image and video processing, pp. 325–335. Academic Press, London (2000)Google Scholar
  6. 6.
    Zheng, J., Valavanis, K.P., Gauch, J.M.: Noise removal from color images. Journal of Intelligent and Robotic Systems 7, 257–285 (1993)CrossRefGoogle Scholar
  7. 7.
    Lukac, R., Smolka, B., Plataniotis, K.N.: Sharpening vector median filters. Signal Processing 87, 2085–2099 (2007)CrossRefGoogle Scholar
  8. 8.
    Kenney, C., Deng, Y., Manjunath, B.S., Hewer, G.: Peer group image enhancement. IEEE Transactions on Image Processing 10, 326–334 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Deng, Y., Kenney, S., Moore, M.S., Manjunath, B.S.: Peer group filtering and perceptual color image quantization. In: Proceedings of the IEEE Int. Symp. on Circuits and Systems (ISCAS), Orlando, FL, vol. 4, pp. 21–24 (1999)Google Scholar
  10. 10.
    Astola, J., Haavisto, P., Neuvo, Y.: Vector median filters. Proc. of the IEEE 78, 678–689 (1990)CrossRefGoogle Scholar
  11. 11.
    Smolka, B., Chydzinski, A.: Fast detection and impulsive noise removal in color images. Real-Time Imaging 11, 389–402 (2005)CrossRefGoogle Scholar
  12. 12.
    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 2007

Authors and Affiliations

  • Bogdan Smolka
    • 1
  1. 1.Silesian University of Technology, Department of Automatic Control, Akademicka 16, 44-100 GliwicePoland

Personalised recommendations