Spatio-temporal markov random field for video denoising

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2007, Pages

  Chen, Jia ^a   Tang, Chi Keung ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; MATHEMATICAL MODELS; MOTION ESTIMATION; OPTIMIZATION; PIXELS; SPURIOUS SIGNAL NOISE; STATISTICAL METHODS;

LIKELIHOOD MODELS; MULTI-FRAME ALGORITHMS; VIDEO DENOISING;

MARKOV PROCESSES;

EID: 35148837656     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2007.383261     Document Type: Conference Paper

References (31)

1. E. P. Bennett and L. McMillan. Video enhancement using per-pixel virtual exposures. In ACM SIGGRAPH, 2005.
2. J. Boulanger, C. Kervrann, and P. Bouthemy. Spacetime adaptation for patch-based image sequence restoration. Technical report, 2006.
3. Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11): 1222-1239, 2001.
4. T. Brox, A. Bruhn, N. Papenberg, and J. Weickert. Highly accurate optic flow computation with theoretically justified warping. In International Journal of Computer Vision, volume 67, pages 141-158, April 2006.
5. T. Brox, A. Bruhn, and J. Weickert. Variational motion segmentation with level sets. In European Conference on Computer Vision (ECCV), 2006.
6. A. Bruhn, J. Weickert, and C. Schnorr. Lucas/kanade meets horn/schunck: Combining local and global optic flow methods. International Journal of Computer Vision, 2005.
7. A. Buades, B. Coll, and J.-M. Morel. Denoising image sequences does not require motion estimation. Technical report, Preprint CMLA 2005-18, 2005.
8. R. Dugad and N. Ahuja. Video denoising by combining kalman and wiener estimates. In IEEE International Conference on Image Processing, 1999.
9. P. F. Felzenszwalb and D. P. Huttenlocher. Efficient belief propagation for early vision. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume 1, pages I-261-I-268 Vol.1, 2004.
10. T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning: Data mining, inference, and prediction. Springer, 2001.
11. B. K. Horn and B. G. Schunck. Determining optical flow. Artificial Intelligence, 17:185-203, 1981.
12. http://media.xiph.org/video/derf/. Xiph.org test media, 2006.
13. A. Kokaram and S. J. Godsill. Mcmc for joint noise reduction and missing data treatement in degraded video. IEEE Transactions on Signal Processing, Special Issue on MCMC, 50:189-205, 2002.
14. X. Lan, S. Roth, D. Huttenlocher, and M. J. Black. Efficient belief propagation with learned higher-order markov random fields. In European Conference on Computer Vision (ECCV), 2006.
15. S. H. Lee and M. G. Kang. Spatio-temporal video filtering algorithm based on 3-d anisotropic diffusion equation. In The IEEE International Conference on Image Processing (ICIP), 1998.
16. Liu, W. T. Freeman, R. Szeliski, and S. B. Kang. Noise estimation from a single image. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2006.
17. B. D. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision. In Proceedings of Imaging Understanding Workshop, pages 121-130, 1981.
18. T. M. Moldovan, S. Roth, and M. J. Black. Denoising archival films using a learned bayesian model. In The IEEE International Conference on Image Processing (ICIP), 2006.
19. P. Perona and J. Malik. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7): 629-639, 1990.
20. J. Portilla, V. Strela, M. Wainwright, and E. P. Simoncelli. Image denoising using scale mixtures of gaussians in the wavelet domain. IEEE Transactions on Image Processing, 2003.
21. N. Rajpoot, Z. Yao, and R. Wilson. Adaptive wavelet restoration of noisy video sequences. In The IEEE International Conference on Image Processing (ICIP), 2004.
22. S. Roth and M. J. Black. Fields of experts: A framework for learning image priors. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume 2, pages 860-867, 2005.
23. P. Sand and S. Teller. Particle video: Long-range motion estimation using point trajectories. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2006.
24. I. W. Selesnick and K. Y. Li. Video denoising using 2d and 3d dual-tree complex wavelet transforms. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2004.
25. J. Shi and C. Tomasi. Good features to track. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1994.
26. E. Simoncelli, E. H. Adelson, and D. J. Heeger. Probability distributions of optical flow. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1991.
27. O. Williams, M. Isard, and J. MacCormick. Estimating disparity and occlusions in stereo video sequences. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2005.
28. J. Xiao, H. Cheng, H. Sawhney, C. Rao, and M. Isnardi. Bilateral filtering-based optical flow estimation with occlusion detection. In European Conference on Computer Vision (ECCV), 2006.
29. V. Zlokolica and W. Philips. Motion- and detail-adaptive denoising of video. In Proceedings of SPIE - Image Processing: Algorithms and Systems IIIVladimir, 2004.
30. V. Zlokolica, A. Pizurica, and W. Philips. Wavelet-domain video denoising based on reliability measures. IEEE Transactions on Circuits and Systems for Video Technology, 16(8):993- 1007, 2006.
31. V. Zlokolica, A. Pizurica, E. Vansteenkiste, and W. Philips. Spatio-temporal approach for noise estimation. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2006.