Video Denoising Algorithm in Sliding 3D DCT Domain
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.
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- 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
- 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.Kingsbury, N.: Complex Wavelets and Shift Invariance., available by the, http://ece-www.colorado.edu/~fmeyer/Classes/ECE-5022/Projects/kingsbury1.pdf
- 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.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.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.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.Rao, K., Yip, P.: Discrete Cosine Transform: Algorithm, Advantages, Applications. Academic Press, New York (1990)Google Scholar
- 11.Clarke, R.J.: Digital Compression of Still Images and Video. Academic Press, London (1995)Google Scholar
- 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.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