Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2013 (2013), Article ID 438147, 10 pages
http://dx.doi.org/10.1155/2013/438147
Research Article

Video Denoising Based on a Spatiotemporal Kalman-Bilateral Mixture Model

College of Information System and Management, National University of Defense Technology, Changsha, Hunan 410073, China

Received 19 August 2013; Accepted 16 September 2013

Academic Editors: W. Su and J. Tian

Copyright © 2013 Chenglin Zuo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. C. Brailean, R. P. Kleihorst, S. Efstratiadis et al., “Noise reduction filters for dynamic image sequences: a review,” Proceedings of the IEEE, vol. 83, no. 9, pp. 1272–1292, 1995. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Buades, B. Coll, and J. M. Morel, “A non-local algorithm for image denoising,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), vol. 2, pp. 60–65, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. V. Karnati, M. Uliyar, and S. Dey, “Fast non-local algorithm for image denoising,” in Proceedings of IEEE International Conference on Image Processing (ICIP '09), pp. 3873–3876, November 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. M. Mahmoudi and G. Sapiro, “Fast image and video denoising via non-local means of similar neighborhoods,” IEEE Signal Processing Letters, vol. 12, no. 12, pp. 839–842, 2005. View at Publisher · View at Google Scholar · View at Scopus
  5. W. Yin, H. Zhao, G. Li, G. Wang, and G. Teng, “A block based temporal spatial nonlocal mean algorithm for video denoising with multiple resolution,” in Proceedings of the 6th International Conference on Signal Processing and Communication Systems (ICSPCS '12), pp. 1–4, December 2012.
  6. A. Buades, B. Coll, and J. M. Morel, “Nonlocal image and movie denoising,” International Journal of Computer Vision, vol. 76, no. 2, pp. 123–139, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674–693, 1989. View at Publisher · View at Google Scholar · View at Scopus
  8. I. Daubechies, “Orthonormal bases of compactly supported wavelets,” Communications on Pure and Applied Mathematics, vol. 41, no. 7, pp. 909–996, 1988. View at Publisher · View at Google Scholar
  9. V. Zlokolica, A. Pizurica, and W. Philips, “Wavelet-domain video denoising based on reliability measures,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 16, no. 8, pp. 993–1007, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. S. M. Mahbubur Rahman, M. Omair Ahmad, and M. N. S. Swamy, “Video denoising based on inter-frame statistical modeling of wavelet coefficients,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 17, no. 2, pp. 187–198, 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. L. Jovanov, A. Pizurica, S. Schulte et al., “Combined wavelet-domain and motion-compensated video denoising based on video codec motion estimation methods,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 19, no. 3, pp. 417–421, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. F. Luisier, T. Blu, and M. Unser, “SURE-LET for orthonormal wavelet-domain video denoising,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 20, no. 6, pp. 913–919, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. K. Dabov, A. Foi, and K. Egiazarian, “Video denoising by sparse 3D transform-domain collaborative filtering,” in Proceedings of the 5th European Signal Processing Conference (EUSIPCO '07), pp. 145–149, September 2007.
  14. S. Yu, M. O. Ahmad, and M. N. S. Swamy, “Video denoising using motion compensated 3-D wavelet transform with integrated recursive temporal filtering,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 20, no. 6, pp. 780–791, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. G. Varghese and Z. Wang, “Video denoising based on a spatiotemporal gaussian scale mixture model,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 20, no. 7, pp. 1032–1040, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Maggioni, G. Boracchi, A. Foi et al., “Video denoising, deblocking, and enhancement through separable 4-D nonlocal spatiotemporal transforms,” IEEE Transactions on Image Processing, vol. 21, no. 9, pp. 3952–3966, 2012. View at Publisher · View at Google Scholar
  17. R. E. Kalman, “A new approach to linear filtering and prediction problems,” Journal of Basic Engineering, vol. 82, no. 1, pp. 35–45, 1960. View at Publisher · View at Google Scholar
  18. C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proceedings of IEEE 6th International Conference on Computer Vision, pp. 839–846, January 1998. View at Scopus
  19. L. Guo, O. C. Au, M. Ma, and Z. Liang, “Temporal video denoising based on multihypothesis motion compensation,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 17, no. 10, pp. 1423–1429, 2007. View at Publisher · View at Google Scholar · View at Scopus
  20. H. Ji, C. Liu, Z. Shen, and Y. Xu, “Robust video denoising using Low rank matrix completion,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '10), pp. 1791–1798, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. J. R. Jain and A. K. Jain, “Displacement measurement and its application in interframe image coding,” IEEE Transactions on Communications, vol. 29, no. 12, pp. 1799–1808, 1981. View at Google Scholar · View at Scopus
  22. Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Processing Letters, vol. 9, no. 3, pp. 81–84, 2002. View at Publisher · View at Google Scholar · View at Scopus
  23. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600–612, 2004. View at Publisher · View at Google Scholar · View at Scopus
  24. Z. Wang, L. Lu, and A. C. Bovik, “Video quality assessment based on structural distortion measurement,” Signal Processing, vol. 19, no. 2, pp. 121–132, 2004. View at Publisher · View at Google Scholar · View at Scopus
  25. Z. Wang and A. C. Bovik, “Mean squared error: love it or leave it? A new look at signal fidelity measures,” IEEE Signal Processing Magazine, vol. 26, no. 1, pp. 98–117, 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. Original codes of ST-GSM, https://ece.uwaterloo.ca/~z70wang/research/stgsm/.
  27. Original codes of VBM3D, http://www.cs.tut.fi/~foi/GCF-BM3D/.
  28. Video Sequence Database, http://www.cipr.rpi.edu/resource/sequences.