Journal of Real-Time Image Processing

, Volume 16, Issue 2, pp 477–489 | Cite as

Fast spatio-temporal digital paths video filter

  • Marek SzczepanskiEmail author
Original Research Paper


This paper describes a novel fast 3D filtering technique for enhancement of color video sequences using digital paths created on the image grid extended to a spatio-temporal domain. Numerous modifications improved impulsive noise filtering efficiency and cope with video artifacts such as Gaussian, impulsive and grain noise and still preserves and even enhances edges. It can even remove block compression artifacts and video flickering. Simulations show that it performs well under PSNR and SSIM metrics. It gives particularly good results for mixed Gaussian and impulse noise—PSNR is approximately 3 dB better than VMF3D and my previous spatial filters. The new algorithm allows video processing in real time for low resolution images; in the preliminary simulations, the processing rate of over 50 fps for the CIF (CIF standard: 352 \(\times\) 288 pixel) video sequences was obtained.


Video enhancement Noise reduction Digital paths Fuzzy filters 



This work was supported by the Polish National Science Center (NCN) under the Grant: DEC-2012/05/B/ST6/03428.


  1. 1.
    Astola, J., Haavisto, P., Neuovo, Y.: Vector median filters. In: IEEE Proc., vol. 78, pp. 678–689 (1990)Google Scholar
  2. 2.
    Bennett, E.P., McMillan, L.: Video enhancement using per-pixel virtual exposures. ACM. Trans. Graph 24(3), 845–852 (2005)CrossRefGoogle Scholar
  3. 3.
    Buades, A., Coll, B., Morel, J.M.: Nonlocal image and movie denoising. Int J Comp Vision 76(2), 123–139 (2008). doi: 10.1007/s11263-007-0052-1 CrossRefGoogle Scholar
  4. 4.
    Buades, A., Coll, B., Morel, J.M.: Non-local means denoising. Image Process (2011). doi: 10.5201/ipol.2011.bcm_nlm zbMATHGoogle Scholar
  5. 5.
    Celebi, M.E., Kingravi, H.A., Aslandogan, Y.A.: Nonlinear vector filtering for impulsive noise removal from color images. CoRR abs/1009.0962 (2010)Google Scholar
  6. 6.
    Cuisenaire, O.: Distance transformations: fast algorithms and applications to medical image processing. PhD thesis, Universite Catholique de Louvain (1999)Google Scholar
  7. 7.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007). doi: 10.1109/TIP.2007.901238 MathSciNetCrossRefGoogle Scholar
  8. 8.
    Dubois, E., Sabri, S.: Noise reduction in image sequences using motion-compensated temporal filtering. IEEE Trans. Commun. 32(7), 826–831 (1984)CrossRefGoogle Scholar
  9. 9.
    Ghoniem, M., Chahir, Y., Elmoataz, A.: Nonlocal video denoising, simplification and inpainting using discrete regularization on graphs. Signal Processing 90(8), 2445–2455 (2010)., special Section on Processing and Analysis of High-Dimensional Masses of Image and Signal Data
  10. 10.
    Maggioni, M., Boracchi, G., Foi, A., Egiazarian, K.: Video denoising, deblocking, and enhancement through separable 4-d nonlocal spatiotemporal transforms. IEEE Trans. Image Process. 21(9), 3952–3966 (2012). doi: 10.1109/TIP.2012.2199324 MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Maggioni, M., Katkovnik, V., Egiazarian, K., Foi, A.: Nonlocal transform-domain filter for volumetric data denoising and reconstruction. IEEE Trans. Image Process. 22(1), 119–133 (2013). doi: 10.1109/TIP.2012.2210725 MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Neumann, J.V.: Theory of Self-Reproducing Automata. University of Illinois Press, Champaign (1966)Google Scholar
  13. 13.
    Pandel, J.: Measuring of flickering artifacts in predictive coded video sequences. In: Ninth International Workshop on Image Analysis for Multimedia Interactive Services, 2008. WIAMIS ’08. pp. 231–234 (2008)Google Scholar
  14. 14.
    Plataniotis, K., Androutsos, D., Venetsanopoulos, A.: Colour image processing using fuzzy vector directional filters. In: Proceedings of the IEEE Workshop on Nonlinear Signal/Image Processing, Greece, pp. 535–538 (1995)Google Scholar
  15. 15.
    Plataniotis, K., Androutsos, D., Venetsanopoulos, A.: Fuzzy adaptive filters for multichannel image processing. Signal Process. J. 55(1), 93–106 (1996)CrossRefzbMATHGoogle Scholar
  16. 16.
    Plataniotis, K., Androutsos, D., Vinayagamoorthy, S., Venetsanopoulos, A.: Color image processing using adaptive multichannel filters. IEEE Trans. Image Proces. 6(7), 933–950 (1997)CrossRefGoogle Scholar
  17. 17.
    Plataniotis, K., Androutsos, D., Venetsanopoulos, A.: Adaptive fuzzy systems for multichannel signal processing. Proc. IEEE 87(9), 1601–1622 (1999)CrossRefGoogle Scholar
  18. 18.
    Ponomaryov, V., Montenegro, H., Rosales, A., Duchen, G.: Fuzzy 3d filter for color video sequences contaminated by impulsive noise. J. Real-Time Image Process (2012). doi: 10.1007/s11554-012-0262-9 Google Scholar
  19. 19.
    Radlak, K., Smolka, B.: Trimmed non-local means technique for mixed noise removal in color images. In: 2013 IEEE International Symposium on Multimedia (ISM), pp. 405–406 (2013)Google Scholar
  20. 20.
    Rosales-Silva, A.J., Gallegos-Funes, F.J., Ponomaryov, V.I.: Fuzzy directional (fd) filter for impulsive noise reduction in colour video sequences. J. Vis. Comun. Image Represent. 23(1), 143–149 (2012). doi: 10.1016/j.jvcir.2011.09.007 CrossRefGoogle Scholar
  21. 21.
    Schmitt, M.: Lecture notes on geodesy and morphological measurements. Proceedings of the Summer School on Morphological Image and Signal Processing, pp. 36–91. Zakopane, Poland (1995)Google Scholar
  22. 22.
    Smolka, B.: Peer group switching filter for impulse noise reduction incolor images. Pattern. Recogn. Lett. 31(6), 484–495 (2010). doi: 10.1016/j.patrec.2009.09.012 CrossRefGoogle Scholar
  23. 23.
    Smolka, B., Wojciechowski, K.: Random walk approach to image enhancement. Signal Process. 81(3), 465–482 (2001)CrossRefzbMATHGoogle Scholar
  24. 24.
    Smolka, B., Szczepanski, M., Plataniotis, K., Venetsanopoulos, A.N.: Fast modified vector median filter. In: Skarbek W (ed) Computer Analysis of Images and Patterns, LNCS, vol. 2124, Springer-Verlag, pp. 570–580 (2001)Google Scholar
  25. 25.
    Smolka, B., Plataniotis, K., Chydzinski, A., Szczepanski, M.: Self-adaptive algorithm of impulsive noise reduction in color images. Patt. Recogn. 35(8), 1771–1784 (2002)CrossRefzbMATHGoogle Scholar
  26. 26.
    Szczepanski, M.: Spatio-temporal filters in video stream processing. In: Burduk R, Kurzyński M, Woźniak M, Żołnierek A (eds) Computer Recognition Systems 4, Advances in Intelligent and Soft Computing, vol. 95, Springer Berlin Heidelberg, pp. 421–430 (2011a). doi:  10.1007/978-3-642-20320-6_44
  27. 27.
    Szczepanski, M.: Spatio-temporal fuzzy fdpa filter. In: Real P, Diaz-Pernil D, Molina-Abril H, Berciano A, Kropatsch W (eds) Computer Analysis of Images and Patterns, Lecture Notes in Computer Science, vol. 6855, Springer Berlin Heidelberg, pp. 316–323 (2011b). doi:  10.1007/978-3-642-23678-5_37
  28. 28.
    Szczepanski, M., Smolka, B., Plataniotis, K., Venetsanopoulos, A.: On the geodesic paths approach to color image filtering. Signal Processing 83(6), 1309–1342 (2003).
  29. 29.
    Szczepanski, M., Smolka, B., Plataniotis, K., Venetsanopoulos, A.: On the distance function approach to color image enhancement. Discrete Applied Mathematics 139(1–3):283–305 (2004).
  30. 30.
    Szczepański, M.: Spatio-temporal digital path approach to video enhancement. In: Choraś RS (ed) Image Processing and Communications Challenges 6, Advances in Intelligent Systems and Computing, vol. 313, Springer International Publishing, pp. 219–226 (2015). doi:  10.1007/978-3-319-10662-5_27
  31. 31.
    Toivanen, P.: New geodesic distance transforms for gray scale images. Patt. Recogn. Lett. 17, 437–450 (1996)CrossRefGoogle Scholar
  32. 32.
    Varghese, G., Wang, Z.: Video denoising based on a spatiotemporal gaussian scale mixture model. IEEE Trans. Circuits Syst. Video Technol. 20(7), 1032–1040 (2010)CrossRefGoogle Scholar
  33. 33.
    Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Faculty of Automatic Control, Electronics and Computer ScienceSilesian University of TechnologyGliwicePoland

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