A Bayesian approach to adaptive video super resolution

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

Volumn , Issue , 2011, Pages 209-216

  Liu, Ce ^a   Sun, Deqing ^b  

^a MICROSOFT RESEARCH   (United States)

^b BROWN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; PATTERN RECOGNITION;

BAYESIAN APPROACHES; EXISTING SYSTEMS; IMAGING CONDITIONS; MOTION MODELS; REAL WORLD VIDEOS; STATE OF THE ART; SUPER RESOLUTION; VIDEO SUPER-RESOLUTION;

OPTICAL RESOLVING POWER;

EID: 80052876469     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995614     Document Type: Conference Paper

References (26)

1. http://www.infognition. com/videoenhancer/, Sep. 2010. Version 1.9.5.
2. S. Baker and T. Kanade. Super-resolution optical flow. Technical report, CMU, 1999.
3. S. Baker and T. Kanade. Limits on super-resolution and how to break them. In CVPR, 2000.
4. B. Bascle, A. Blake, and A. Zisserman. Motion deblurring and superresolution from an image sequence. In ECCV, 1996.
5. T. Brox, A. Bruhn, N. Papenberg, and J. Weickert. High accuracy optical flow estimation based on a theory for warping. In ECCV, 2004.
6. R. Fransens, C. Strecha, and L. J. Van Gool. Optical flow based super-resolution: A probabilistic approach. CVIU, 106:106-115, 2007.
7. R. C. Gonzalez and R. E. Woods. Digital Image Processing. Addison-Wesley, 2nd edition, 2001.
8. R. Hardie, K. Barnard, and E. Armstrong. Joint MAP registration and high-resolution image estimation using a sequence of undersampled images. IEEE TIP, 6(12):1621-1633, Dec. 1997.
9. B. Horn and B. Schunck. Determining optical flow. Artificial Intelligence, 16:185-203, Aug. 1981.
10. M. Irani and S. Peleg. Improving resolution by image registration. CVGIP, 53:231-239, 1991.
11. N. Joshi, R. Szeliski, and D. Kriegman. PSF estimation using sharp edge prediction. In CVPR, 2008.
12. D. Kundur and D. Hatzinakos. Blind image deconvolution. Signal Proc. Magazine, IEEE, 13(3):43-64, 1996.
13. A. Levin, R. Fergus, F. Durand, and W. T. Freeman. Image and depth from a conventional camera with a coded aperture. ACM TOG, 26, 2007.
14. A. Levin, Y. Weiss, F. Durand, and W. Freeman. Understanding and evaluating blind deconvolution algorithms. In CVPR, 2009.
15. Z. Lin and H.-Y. Shum. Fundamental limits of reconstruction based superresolution algorithms under local translation. TPAMI, 26:83-97, 2004.
16. C. Liu. Beyond pixels: exploring new representations and applications for motion analysis. PhD thesis, MIT, 2009.
17. C. Liu and W. T. Freeman. A high-quality video denoising algorithm based on reliable motion estimation. In ECCV, 2010.
18. C. Liu, R. Szeliski, S. B. Kang, C. L. Zitnick, and W. T. Freeman. Automatic estimation and removal of noise from a single image. TPAMI, 30(2):299-314, 2008.
19. N. Nguyen, G. Golub, and P. Milanfar. Blind restoration/superresolution with generalized cross-validation using gausstype quadrature rules. In ICSSC, 1999.
20. S. C. Park, M. K. Park, and M. G. Kang. Super-resolution image reconstruction: a technical overview. Signal Proc. Magazine, IEEE, 20(3):21-36, 2003.
21. S. Roth. High-Order Markov Random Fields for Low-Level Vision. PhD thesis, Brown University, 2007.
22. R. Schultz and R. Stevenson. Extraction of high-resolution frames from video sequences. IEEE TIP, 5(6):996-1011, June 1996.
23. Q. Shan, Z. Li, J. Jia, and C.-K. Tang. Fast image/video upsampling. ACM TOG, 27(5):153, 2008.
24. F. Sroubek, G. Cristobal, and J. Flusser. Simultaneous superresolution and blind deconvolution. Journal of Physics: Conference Series, 124(1), 2008.
25. H. Takeda, P. Milanfar, M. Protter, and M. Elad. Super-resolution without explicit subpixel motion estimation. IEEE TIP, 18(9):1958-1975, Sep. 2009.
26. R. Y. Tsai and T. S. Huang. Multiframe image restoration and registration. In Advances in CVIP, 1984.