Pixel Domain Spatio-temporal Denoising for Archive Videos

  • M. Kemal Güllü
  • Oğuzhan Urhan
  • Sarp Ertürk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4263)


A new pixel domain spatio-temporal video noise filter for archive film restoration has been proposed in this paper. The proposed filtering method takes motion changes and spatial information into account. Firstly, temporal filtering is carried out considering temporal changes adaptively. Afterwards, interpolation between degraded and temporally filtered images is carried out to preserve edge information using local standard deviation values. With respect to pixel domain techniques proposed in the literature, the proposed method gives better results for various test videos and particularly provides superior results for archive film.


Local Motion Wavelet Domain Wiener Filter Pixel Domain IEEE Signal Processing Letter 
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.
    Malfait, M., Roose, D.: Wavelet-Based Image Denoising Using a Markov Random Field a Priori Model. IEEE Trans. on Image Processing 6(4), 549–565 (1997)CrossRefGoogle Scholar
  2. 2.
    Kazubek, M.: Wavelet Domain Image Denoising by Thresholding and Wiener Filtering. IEEE Signal Processing Letters 10(11), 324–326 (2003)CrossRefGoogle Scholar
  3. 3.
    Van De Ville, D., Nachtegael, M., Van der Weken, D., Kerre, E.E., Philips, W., Lemahieu, I.: Noise Reduction by Fuzzy Image Filtering. IEEE Trans. on Fuzzy Systems 11(4), 429–436 (2003)CrossRefGoogle Scholar
  4. 4.
    Sadhar, S.I., Rajagopalan, A.N.: Image Estimation in Film-Grain Noise. IEEE Signal Processing Letters 12(3), 238–241 (2005)CrossRefGoogle Scholar
  5. 5.
    Ozkan, M.K., Sezan, I., Tekalp, A.M.: Adaptive Motion Compensated Filtering of Noisy Image Sequences. IEEE Trans. on Circuits and Systems for Video Technology 3(4), 277–290 (1993)CrossRefGoogle Scholar
  6. 6.
    Dugad, R., Ajuha, N.: Video Denoising by Combining Kalman and Wiener Estimates. In: IEEE Int. Conf. on Image Processing, Kobe, Japan, vol. 4, pp. 152–161 (1999)Google Scholar
  7. 7.
    Zlokolica, V., Philips, W., Van De Ville, D.: A New Non-Linear Filter for Video Processing. In: 3rd IEEE Benelux Signal Processing Symposium (SPS-2002), Leuven, Belgium, pp. 13–16 (2002)Google Scholar
  8. 8.
    Selesnick, I.W., Li, K.Y.: Video Denoising Using 2d and 3d Dual-Tree Complex Wavelet Transforms. In: Wavelet Applications in Signal and Image Processing (SPIE 5207), San Diego, vol. 5207, pp. 607–618 (2003)Google Scholar
  9. 9.
    Pizurica, A., Zlokolica, V., Philips, W.: Combined Wavelet Domain and Temporal Video Denoising. In: IEEE Conf. on Advanced Video and Signal Based Surveillance (AVSS 2003), pp. 334–341 (2003)Google Scholar
  10. 10.
    Gupta, N., Swamy, M.N.S., Plotkin, E.I.: Low-Complexity Video Noise Reduction in Wavelet Domain. In: IEEE 6th Workshop on Multimedia Signal Processing, pp. 239–242 (2004)Google Scholar
  11. 11.
    Chan, T.-W., Au, O.C., Chong, T.S., Chau, W.-S.: A Novel Content-Adaptive Video Denoising Filter. In: IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Philadelphia, PA, USA, vol. 2, pp. 649–652 (2005)Google Scholar
  12. 12.
    Lim, J.S.: Two-Dimensional Signal and Image Processing, pp. 536–540. Prentice-Hall, Englewood Cliffs (1990)Google Scholar
  13. 13.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C, 2nd edn. Cambridge Univ. Press, New York (1992)MATHGoogle Scholar
  14. 14.
    Brailean, J., Kleihorst, R., Efstratiadis, S., Katsaggalegos, A., Lagendijk, A.: Noise Reduction Filters for Dynamic Image Sequences: A Review. Proc. of IEEE 83(9), 1272–1292 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • M. Kemal Güllü
    • 1
  • Oğuzhan Urhan
    • 1
  • Sarp Ertürk
    • 1
  1. 1.Kocaeli University Laboratory of Image and Signal processing (KULIS), Electronics and Telecom. Eng. Dept.University of KocaeliKocaeliTurkey

Personalised recommendations