High-resolution images from low-resolution compressed video

IEEE Signal Processing Magazine

Volumn 20, Issue 3, 2003, Pages 37-48

  Segall, C Andrew ^a   Molina, Rafael ^b   Katsaggelos, Aggelos K ^c  

^a NORTHWESTERN UNIVERSITY   (United States)

^b UNIVERSITY OF GRANADA   (Spain)

^c EVANSTON HOSPITAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CAMERAS; IMAGE SENSORS; MATHEMATICAL MODELS; MOTION COMPENSATION; VECTORS;

HIGH-RESOLUTION IMAGES;

IMAGE COMPRESSION;

EID: 85032751273     PISSN: 10535888     EISSN: None     Source Type: Journal    
DOI: 10.1109/MSP.2003.1203208     Document Type: Article

References (36)

1. S. Chaudhuri, Ed., Super-Resolution Imaging. Norwell, MA: Kluwer, 2001.
2. S. Borman and R. Stevenson, "Spatial resolution enhancement of low-resolution image sequences. A comprehensive review with directions for future research," Lab. for Image and Sugnal Analysis, Univ. Notre Dame, Tech. Rep., 1998.
3. A.N. Netravali and B.G. Haskell, Digital Picture - Representation and Compression. New York: Plenum, 1995.
4. V. Bhaskaran and K. Konstantinides, Image and Video Compression Standards. Norwell, MA: Kluwer, 1995.
5. Y. Altunbasak, A.J. Patti, and R.M. Mersereau, "Super-resolution still and video reconstruction from mpeg-coded video," IEEE Transa. Circuits Syst. Video Technol., vol. 12, pp. 217-226, Apr. 2002.
6. Y. Altunbasak and A.J Patti, "A maximum a posteriori estimator for high resolution video reconstruction from mpeg video," in Proc. IEEE Int. Conf. Image Processing, 2000, vol. 2, pp. 649-652.
7. B.K. Gunturk, Y. Antunbasak, and R. Mersereau, "Bayesian resolution-enhancement framework for transform-coded video," in Proc. IEEE Int. Conf. Image Processing, 2001, vol. 2, pp. 41-44.
8. A.J. Patti and Y. Altunbasak, "Super-resolution image estimation for transform coded video with application to MPEG." in Proc. IEEE Int. Conf. Image Processing, 1999, vol. 3, pp. 179-183.
9. C.A. Segall, A.K. Katsaggelos, R. Molina, and J. Mateos, "Super-resolution from compressed video," in Super-Resolution Imaging, S. Chaudhuri, Ed. Norwell, MA: Kluwer, 2001, pp. 211-242.
10. C.A. Segall, R. Molina, A.K. Katsaggelos, and J. Mateos, "Bayesian high-resolution reconstruction of low-resolution compressed video," in Proc. IEEE Int. Conf. Image Processing, 2001, vol. 2, pp. 25-28.
11. M.A. Robertson and R.L. Stevenson, "DCT quantization noise in compressed images," in Proc. IEEE Int. Conf Image Processing, 2001, vol. 1, pp. 185-188.
12. D. Chen and R.R. Schultz, "Extraction of high-resolution video stills from mpeg image sequences," in Proc. IEEE Int. Conf. Image Processing, 1998, vol. 2, pp. 465-469.
13. B.K. Gunturk, Y. Altunbasak, and R.M. Mersereau, "Multiframe resolution-enhancement methods for compressed video," IEEE Signal Processing Lett., vol. 9, pp. 170-174, June 2002.
14. J. Mateos, A.K. Katsaggelos, and R. Molina, "Resolution enhancement of compressed low resolution video," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, 2000, vol. 4, pp. 1919-1922.
15. J. Mateos, A.K. Katsaggelos, and R. Molina, "Simultaneous motion estimation and resolution enhancement of compressed low resolution video," in Proc. IEEE Int. Conf. Image Processing, 2000, vol. 2, pp. 653-656.
16. S.C. Park, M.G. Kang, C.A. Segall, and A.K. Katsaggelos, "Spatially adaptive high-resoltuion image reconstruction of low-resolution dct-based compressed images," in Proc. IEEE Int. Conf. Image Processing, 2002, vol. 2, pp. 861-864.
17. S.C. Park, M.G. Kang, C.A. Segall, and A.K. Katsaggelos, "Spatially adaptive high-resoltuion image reconstruction of low-resolution dct-based compressed images," IEEE Trans. Image Processing, submitted for publication.
18. C.A. Segall, R. Molina, A.K. Katsaggelos, and J. Mateos, "Reconstruction of high-resolution image frames from a sequence of low-resolution and compressed observations," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, 2002, vol. 2, pp. 1701-1704.
19. C. A. Segall, A. K. Katsaggelos, R. Molina, and J. Mateos, "Bayesian resolution enhancement of compressed video," IEEE Trans. Image Processing, submitted for publication.
20. S.C. Park, M.G. Kang, C.A. Segall, and A.K. Katsaggelos, "High-resolution image reconstruction of low-resolution dct-based compressed images," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, 2002, vol. 2, pp. 1665-1668.
21. D.G. Luenberger, Linear and Nonlinear Programming. Reading, MA: Addison-Wesley, 1984.
22. B.D. Lucas and T. Kanade, "An iterative image registration technique with an application to stereo vision," in Proc. Imaging Understanding Workshop, 1981, pp. 121-130.
23. D.C. Youla and H. Webb, "Image restoration by the method od convex projections: Part I - Theory," IEEE Trans. Med. Imag., vol. MI-1, no. 2, pp. 81-94, 1982.
24. M.C. Choi, Y. Yang, and N.P. Galatsanos, "Multichannel regularized recovery of compressed video sequences," IEEE Trans. Circuits Syst. II, vol. 48, pp. 376-387, 2001.
25. E.P. Simoncelli, E.H. Adelson, and D.J. Heeger, "Probability distributions of optical flow," in Proc. IEEE Computer Soc. Conf. Computer Vision and Pattern Recognition, 1991, pp. 310-315.
26. O. Nestares and R. Navarro, "Probabilistic estimation of optical flow in multiple band-pass directional channels," Image and Vision Computing, vol. 19, pp. 339-351, 2001.
27. M.R. Luettgen, W. Clem Karl, and A.S. Willsky, "Efficient multiscale regularization with applications to the computation of optical flow," IEEE Trans. Image Processing, vol. 3, pp. 41-64, 1994.
28. J. Chamorro-Martínez, "Desarrollo de modelos computacionales de representación de secuencias de imágenes y su aplicación a la estimación de movimiento" (in Spanish), Ph.D. dissertation, Univ. of Granada, 2001.
29. J. Konrad and E. Dubois, "Bayesian estimation of motion vector fields," IEEE Trans. Pattern Anal. Machine Intell., vol. 14, no. 9, pp. 910-927, 1992.
30. M. Irani and S. Peleg, "Motion analysis for image enhancement: Resolution, occlusion, and transparency," J. Visual Commun. Image Process., vol. 4, pp. 324-335, 1993.
31. M. Irani, B. Rousso, and S. Peleg, "Computing occluding and transparent motions," Int. J. Comput. Vision, vol. 12, no. 1, pp. 5-16, Jan. 94.
32. Y. Weiss and E.H. Adelson, "A unified mixture framework for motion segmentation: incorporating spatial coherence and estimating the number of models," in Proc. IEEE Computer Soc. Conf. Computer Vision and Pattern Recognition, 1996, pp. 321-326.
33. S. Baker and T. Kanade, "Limits on super-resolution and how to break them," IEEE Trans. Pattern Anal. Machine Intell., vol. 24, pp. 1167-1183, 2002.
34. W.T. Freeman, E.G. Pasztor, and O.T. Carmichael, "Learning low-level vision,"Int. J. Computer Vision, vol. 40, pp. 24-57, 2000.
35. W.T. Freeman, T.R. Jones, and E.C. Pasztor, "Example-based super-resolution," IEEE Comput. Graph. Appli., pp. 56-65, 2002.
36. D. Capel and A. Zisserman, "Super-resolution from multiple views using learnt image models," in Proc. IEEE Computer Soc. Conf. Computer Vision and Pattern Recognition, 2001, vol. 2, pp. 627-634.