Video Denoising Algorithm in Sliding 3D DCT Domain

  • Dmytro Rusanovskyy
  • Karen Egiazarian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3708)


The problem of denoising of video signals corrupted by additive Gaussian noise is considered in this paper. A novel 3D DCT-based video-denoising algorithm is proposed. Video data are locally filtered in sliding/running 3D windows (arrays) consisting of highly correlated spatial layers taken from consecutive frames of video. Their selection is done by the use of a block matching or similar techniques. Denoising in local windows is performed by a hard thresholding of 3D DCT coefficients of each 3D array. Final estimates of reconstructed pixels are obtained by a weighted average of the local estimates from all overlapping windows. Experimental results show that the proposed algorithm provides a competitive performance with state-of-the-art video denoising methods both in terms of PSNR and visual quality.


Video Sequence Video Signal Consecutive Frame Image Denoising Block Match 
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.
    Brailean, J.C., Kleihorst, R.P., Efstratiadis, S., Katsaggelos, A.K., Lagendijk, R.L.: Noise Reduction Filters for Dynamic Image Sequences: A Review. IEEE Proc. 83(9) (September 1995)Google Scholar
  2. 2.
    Donoho, D.L.: De-noising by soft-thresholding. IEEE Trans. on Information Theory 41(3), 613–627 (1995)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Sendur, L., Selesnick, I.W.: Bivariage Shrinkage Functions for Wavelet-Based Denoising Exploiting Interscale Dependency. IEEE Trans. on Signal Proc. 50(11), 2745–2756 (2002)CrossRefGoogle Scholar
  4. 4.
    Coifman, R., Donoho, D.: Translation Invariant de-noising. In: Lecture Notes in Statistics: Wavelets and Statistics, pp. 125–150. Springer, New York (1995)Google Scholar
  5. 5.
    Kingsbury, N.: Complex Wavelets and Shift Invariance., available by the,
  6. 6.
    Selesnick, W.I., Li, K.Y.: Video denoising using 2d and 3d dualtree complex wavelet transforms. In: Proc. SPIE Wavelet Applications in Signal and Image Processing, San Diego, August 2003, vol. 5207 (2003)Google Scholar
  7. 7.
    Zlokolica, V., Pizurica, A., Philips, W.: Wavelet Domain Noise-Robust Motion Estimation and Noise Estimation for Video Denoising. In: First International Workshop on Video Processing and Quality Metrics for Consumer Electronics, Scotssdale, Arizona, USA, January 23-25 (2005)Google Scholar
  8. 8.
    Yaroslavsky, L., Egiazarian, K., Astola, J.: Transform domain image restoration methods: review, comparison and interpretation. TICSP Series #9. TUT, Tampere, Finland (December 2000) ISBN 952-15-0471-4Google Scholar
  9. 9.
    Öktem, R., Yaroslavsky, L., Egiazarian, K.: Signal and Image Denoising in Transform Domain and Wavelet Shrinkage: A Comparative Study. In: Proc. of EUSIPCO 1998, Rhodes, Greece (September 1998)Google Scholar
  10. 10.
    Rao, K., Yip, P.: Discrete Cosine Transform: Algorithm, Advantages, Applications. Academic Press, New York (1990)Google Scholar
  11. 11.
    Clarke, R.J.: Digital Compression of Still Images and Video. Academic Press, London (1995)Google Scholar
  12. 12.
    Egiazarian, K., Katkovnik, V., Öktem, H., Astola, J.: Transform-based denoising with window size adaptive to unknown smoothness of the signal. In: Proc. of First International Workshop on Spectral Techniques and Logic Design for Future Digital Systems (SPECLOG), Tampere, Finland, June 2000, pp. 409–430 (2000)Google Scholar
  13. 13.
    Gupta, N., Plotkin, E., Swamy, M.: Bayesian Algorithm for Video Noise Reduction in the Wavelet Domain. In: IEEE International Symposium on Circuits and Systems, ISCAS 2005, Kobe, Japan, May 23-26 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Dmytro Rusanovskyy
    • 1
  • Karen Egiazarian
    • 1
  1. 1.Institute of Signal ProcessingTampere University of TechnologyFinland

Personalised recommendations