Journal of Intelligent and Robotic Systems

, Volume 42, Issue 4, pp 361–391 | Cite as

A Statistically-Switched Adaptive Vector Median Filter

  • Rastislav Lukac
  • Konstantinos N. Plataniotis
  • Anastasios N. Venetsanopoulos
  • Bogdan Smolka
Article

Abstract

This paper presents a new cost-effective, adaptive multichannel filter taking advantage of switching schemes, robust order-statistic theory and approximation of the multivariate dispersion. Introducing the statistical control of the switching between the vector median and the identity operation, the developed filter enhances the detail-preserving capability of the standard vector median filter. The analysis and experimental results reported in this paper indicate that the proposed method is capable of detecting and removing impulsive noise in multichannel images. At the same time, the method is computationally efficient and provides excellent balance between the noise attenuation and signal-detail preservation. Excellent performance of the proposed method is tested using standard test color images as well as real images related to emerging virtual restoration of artworks.

Keywords

nonlinear image processing color image filtering adaptive filter design order-statistic theory virtual restoration of artworks 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Anderson, T. W.: An Introduction to Multivariate Statistical Analysis, 2nd edn, New York, Wiley, 1984. Google Scholar
  2. 2.
    Astola, J., Haavisto, P., and Neuvo, Y.: Vector median filters, Proc. IEEE 78 (1990), 678–689. Google Scholar
  3. 3.
    Astola, J. and Kuosmanen, P.: Fundamentals of Nonlinear Digital Filtering, CRC Press, Boca Raton, FL, 1997. Google Scholar
  4. 4.
    Barni, M., Bartolini, F., and Capellini, V: Image processing for virtual restoration of artworks, IEEE Multimedia (2000), 34–37. Google Scholar
  5. 5.
    Barni, M., Cappelini, V., and Mecocci, A.: Fast vector median filter based on Euclidean norm approximation, IEEE Signal Processing Lett. 1 (1994), 92–94. Google Scholar
  6. 6.
    Beghdadi, A. and Khellaf, K.: A noise-filtering method using a local information measure, IEEE Trans. Image Processing 6 (1997), 879–882. Google Scholar
  7. 7.
    Chapuis, R., Aufrere, R., and Chausse, F.: Accurate road following and reconstruction by computer vision, IEEE Trans. Intelligent Transport. Systems 3 (2002), 261–270. Google Scholar
  8. 8.
    Chen, T. and Wu, H. R.: Adaptive impulse detection using center-weighted median filters, IEEE Signal Processing Lett. 8 (2001), 1–3. Google Scholar
  9. 9.
    Eng, H. L. and Ma, K. K.: Noise adaptive soft-switching median filter, IEEE Trans. Image Processing 10 (2001), 242–251. Google Scholar
  10. 10.
    Forsyth, D. A. and Ponce, J.: Computer Vision: A Moder Approach, Prentice-Hall, Englewood Cliffs, NJ, 2002. Google Scholar
  11. 11.
    Gabbouj, M. and Cheickh, A.: Vector median–vector directional hybrid filter for color image restoration, in: Proc. EUSIPCO-1996, pp. 879–881. Google Scholar
  12. 12.
    Karakos, D. G. and Trahanias, P. E.: Generalized multichannel image-filtering structure, IEEE Trans. Image Processing 6 (1997), 1038–1045. Google Scholar
  13. 13.
    Kumar, R., Sawhney, R., Samarasekera, S., Hsu, S., Tao, H., Guo, Y., Hanna, K., Pope, A., Wildes, R., Hirvonen, D., Hansen, M., and Burt, P.: Aerial video surveillance and exploitation, Proc. IEEE 89 (2001), 1518–1539. Google Scholar
  14. 14.
    Lee, J. S.: Digital image smoothing and the sigma filter, Computer Vision Graphics Image Processing 24 (1983), 255–269. Google Scholar
  15. 15.
    Li, X., Lu, D., and Pan, Y.: Color restoration and image retrieval for Donhunag fresco preservation, IEEE Multimedia (2000), 38–42. Google Scholar
  16. 16.
    Lukac, R.: Adaptive color image filtering based on center-weighted vector directional filters, Multidimensional Systems Signal Processing 15 (2004), 169–196. Google Scholar
  17. 17.
    Lukac, R., Plataniotis, K. N., Smolka, B., and Venetsanopoulos, A. N.: Generalized selection weighted vector filters, EURASIP J. Appl. Signal Processing, Special Issue on Nonlinear Signal and Image Processing (2004), 1870–1885. Google Scholar
  18. 18.
    Lukac, R., Smolka, B., Martin, K., Plataniotis, K. N. and Venetsanopoulos, A. N.: Vector filtering for color imaging, IEEE Signal Processing Magazine, Special Issue on Color Image Processing 22 (2005), 74–86. Google Scholar
  19. 19.
    Lukac, R., Smolka, B., Plataniotis, K. N., and Venetsanopoulos, A. N.: Selection weighted vector directional filters, Computer Vision Image Understanding, Special Issue on Colour for Image Indexing and Retrieval 94 (2004), 140–167. Google Scholar
  20. 20.
    Lucat, L., Siohan, P., and Barba, D.: Adaptive and global optimization methods for weighted vector median filters, Signal Processing Image Commun. 17 (2002), 509–524. Google Scholar
  21. 21.
    Mardia, K. V., Kent, J. T., and Bibby, J. M.: Multivariate Analysis, Academic Press, New York, 1979. Google Scholar
  22. 22.
    Mustonen, S.: A measure for total variability in multivariate normal distribution, Comput. Statistics Data Analysis 23 (1997), 321–334. Google Scholar
  23. 23.
    Nosovsky, R. M.: Choice, similarity and the context theory of classification, J. Experimental Psych. Learning Memory Cognition 10 (1984), 104–114. Google Scholar
  24. 24.
    Pitas, I. and Tsakalides, P.: Multivariate ordering in color image filtering, IEEE Trans. Circuits Systems Video Technol. 1 (1991), 247–259. Google Scholar
  25. 25.
    Pitas, I. and Venetsanopoulos, A. N.: Nonlinear Digital Filters, Principles and Applications, Kluwer Academic Publishers, Dordrecht, 1990. Google Scholar
  26. 26.
    Pitas, I. and Venetsanopoulos, A. N.: Order statistics in digital image processing, Proc. IEEE 80 (1992), 1892–1919. Google Scholar
  27. 27.
    Plataniotis, K. N., Androutsos, D., and Venetsanopoulos, A. N.: Adaptive fuzzy systems for multichannel signal processing, Proc. IEEE 87 (1999), 1601–1622. Google Scholar
  28. 28.
    Plataniotis, K. N. and Venetsanopoulos, A. N.: Color Image Processing and Applications, Springer, Berlin, 2000. Google Scholar
  29. 29.
    Sharma, G. and Trussel, H. J.: Figures of merit for color scanners, IEEE Trans. Image Processing 6 (1997), 990–1001. Google Scholar
  30. 30.
    Tang, K., Astola, J., and Neuvo, Y.: Nonlinear multivariate image filtering techniques, IEEE Trans. Image Processing 4 (1995), 788–798. Google Scholar
  31. 31.
    Trahanias, P. E., Karakos, D., and Venetsanopoulos, A. M.: Directional processing of color images: Theory and experimental results, IEEE Trans. Image Processing 5 (1996), 868–881. Google Scholar
  32. 32.
    Viero, T., Oistamo, K., and Neuvo, Y.: Three-dimensional median related filters for color image sequence filtering, IEEE Trans. Circuits Systems Video Technol. 4 (1994), 129–142. Google Scholar
  33. 33.
    Zhang, S. and Karim, M. A.: A new impulse detector for switching median filters, IEEE Signal Processing Lett. 9 (2002), 360–363. Google Scholar
  34. 34.
    Zheng, J., Valavanis, K. P., and Gauch, J. M.: Noise removal from color images, J. Intelligent Robotic Systems 7 (1993), 257–285. Google Scholar
  35. 35.
    Lukac, R., Plataniotis, K. N., and Smolka, B.: Adaptive color image filter for application in virtual restoration of artworks, IEEE Transactions on Multimedia, submitted. Google Scholar

Copyright information

© Springer 2005

Authors and Affiliations

  • Rastislav Lukac
    • 1
  • Konstantinos N. Plataniotis
    • 1
  • Anastasios N. Venetsanopoulos
    • 1
  • Bogdan Smolka
    • 2
  1. 1.The Edward S. Rogers Sr. Department of ECEUniversity of TorontoTorontoCanada
  2. 2.Department of Automatic ControlSilesian University of TechnologyGliwicePoland

Personalised recommendations