Spatio-Temporal Filters in Video Stream Processing

  • Marek Szczepanski
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)


An overview of real-time video filtering techniques was presented in this paper. The extension of standard techniques based on temporal Gaussian combined with Fast Digital Paths Approach [8] was presented. Presented technique provides excellent noise suppression ability especially for low light sequences with low computational complexity.


Anisotropic Diffusion Subsequent Frame Reference Video Vector Median Filter Objective Quality Measure 
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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  1. 1.
    Bennett, E.P., McMillan, L.: Video enhancement using per-pixel virtual exposures. ACM Trans. Graph. 24(3), 845–852 (2005)CrossRefGoogle Scholar
  2. 2.
    Dubois, E., Sabri, S.: Noise reduction in image sequences using motion-compensated temporal filtering. IEEE Transactions on Communications 32(7), 826–831 (1984)CrossRefGoogle Scholar
  3. 3.
    Lee, S., Maik, V., Jang, J., Shin, J., Paik, J.: Noise-adaptive spatio-temporal filter for real-time noise removal in low light level images. IEEE Transactions on Consumer Electronics 51, 648–653 (2005)CrossRefGoogle Scholar
  4. 4.
    Lee, S., Kang, M. G.: Spatio-temporal video filtering algorithm based on 3-d anisotropic diffusion equation. In: Proceedings of International Conference on Image Processing, ICIP 1998, vol. 2, pp. 447–450 (1998)Google Scholar
  5. 5.
    Meguro, M., Taguchi, A., Hamada, N.: Data-dependent weighted median filtering with robust motion information for image sequence restoration. In: Proceedings of International Conference on Image Processing, ICIP 1999, vol. 2, pp. 424–428 (1999)Google Scholar
  6. 6.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 629–639 (1990)CrossRefGoogle Scholar
  7. 7.
    Plataniotis, K., Androutsos, D., Venetsanopoulos, A.: Fuzzy adaptive filters for multichannel image processing. Signal Processing Journal 55(1), 93–106 (1996)CrossRefzbMATHGoogle Scholar
  8. 8.
    Szczepanski, M., Smolka, B., Plataniotis, K., Venetsanopoulos, A.: On the geodesic paths approach to color image filtering. Signal Processing 83(6), 1309–1342 (2003)CrossRefzbMATHGoogle Scholar
  9. 9.
    Toivanen, P.: New geodesic distance transforms for gray scale images. Pattern Recognition Letters 17, 437–450 (1996)CrossRefGoogle Scholar
  10. 10.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: ICCV, pp. 839–846 (1998)Google Scholar
  11. 11.
    Viero, T., Neuvo, Y.: Non-moving regions preserving median filters for image sequence filtering. In: IEEE International Conference on Systems Engineering, pp. 245–248 (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marek Szczepanski
    • 1
  1. 1.Institute of Automatic ControlSilesian University of TechnologyPoland

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