Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images

IEEE Transactions on Image Processing

Volumn 6, Issue 12, 1997, Pages 1646-1658

  Elad, Michael ^b   Feuer, Arie ^a,b  

^a IEEE

^b TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

Constrained optimization problems; Estimation; Image restoration; MAP; ML; POCS; Regularization; Supperresolution

Indexed keywords

COMPUTER SIMULATION; ESTIMATION; IMAGE ENHANCEMENT; OPTIMIZATION; PROBABILITY;

MAXIMUM A POSTERIORI (MAP) PROBABILITY ESTIMATOR; MAXIMUM LIKELIHOOD ESTIMATOR; PROJECTION ONTO CONVEX SETS (POCS); SUPERRESOLUTION IMAGE RECONSTRUCTION;

IMAGE RECONSTRUCTION;

EID: 0031332301     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/83.650118     Document Type: Article

References (33)

1. R. C. Gonzalez and P. Wintz, Digital Image Processing. New York: Addison-Wesley, 1987.
2. W. K. Pratt, Digital Image Processing. New York: Wiley, 1991.
3. A. K. Jain, Fundamentals in Digital Image Processing. Englewood Cliffs, NJ: Prentice-Hall, 1989.
4. R. L. Lagendijk and J. Biemond Iterative Identification and Restoration of Images. Boston, MA: Kluwer, 1991.
5. D. C. Youla, "Generalized image restoration by the method of alternating orthogonal projections," IEEE Trans. Circuits Syst., vol. CAS-25, pp. 694-702, 1978.
6. A. K. Katsaggelos, "A multiple input image restoration approach," J. Vis. Commun. Image Representat., vol. 1, pp. 93-103, Sept. 1990.
7. D. C. Ghiglia, "Space-invariant deblurring given N independently blurred images of a common object," J. Opt. Soc. Amer., vol. 1, pp. 398-402, Apr. 1984.
8. B. R. Hunt and O. Kubler, "Karhunen-Loeve multispectral image restoration, Part I: Theory," IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-32, pp. 592-599, June 1984.
9. S. J. Ko and Y. H. Lee, "Nonlinear spatio-temporal noise suppression techniques with applications in image sequence processing," IEEE Int. Symp. CIS, 1991, vol. 5, pp. 662-665.
10. T. S. Huang, Image Sequence Analysis. New York: Springer-Verlag, 1981.
11. _,Image Sequence Processing and Dynamic Scene Analysis. New York: Springer-Verlag, 1983.
12. A. J. Patti, A. M. Tekalp, and M. I Sezan, Image Sequence Restoration and De-Interlacing by Motion-Compensated Kalman Filtering, SPIE, vol. 1903, 1993.
13. T. S. Huang and R. Y. Tsay, "Multiple frame image restoration and registration," in Advances in Computer Vision and Image Processing, vol. 1, T. S. Huang, Ed. Greenwich, CT: JAI, pp. 317-339, 1984.
14. S. P. Kim, N. K. Bose, and H. M Valenzuela, "Recursive reconstruction of high resolution image from noisy undersampled multiframes," IEEE Trans. Acoust., Speech, Signal Processing, vol. 38, pp. 1013-1027, June 1990.
15. N. K. Bose, S. P. Kim, and H. M Valenzuela, "Recursive implementation of total least squares algorithm for image reconstruction from noisy, undersampled multiframes," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), Minneapolis MN, vol. V, pp. 269-272, 1993.
16. H. C. Kim, "High resolution image reconstruction from undersampled multiframes," Ph.D. dissertation, Pennsylvania State Univ., Univ. Park, PA, 1994.
17. A. Papoulis, "Generalized sampling theorem," IEEE Trans. Circuits Syst., vol. CAS-24, pp. 652-654, Nov. 1977.
18. L. J. Yen, "On nonuniform sampling of bandwidth limited signals," IRE Trans. Circuits Theory, vol. 3, pp. 251-257, Apr. 1956.
19. H. Ur and D. Gross, "Improved resolution from sub-pixel shifted pictures," CVGIP: Graph. Models Image Process., vol. 54, pp. 181-186, Mar. 1992.
20. C. Srinivas and M. D. Srinath, "A stochastic model-based approach for simultaneous restoration of multiple miss-registered images," SPIE, vol 1360, pp. 1416-1427, 1990.
21. S. Peleg, D. Keren, and L. Schweitzer, "Improving image resolution using subpixel motion," Pattern Recognit. Lett., vol. 5, pp. 223-226, Mar. 1987.
22. M. Irani and S. Peleg, "Improving resolution by Image Registration," CVGIP: Graph. Models Image Process., vol. 53, pp. 231-239, Mar. 1991.
23. _, "Motion analysis for image enhancement: resolution, occlusion, and transparency," J. VCIR, vol. 4, pp. 324-335, Dec. 1993.
24. A. M. Tekalp, M. K. Ozkan, and M. I. Sezan, "High-resolution image reconstruction from lower-resolution image sequences and space varying image restoration," IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), San Francisco, CA., Mar. 1992, vol. III, pp. 169-172.
25. A. J. Patti, M. I. Sezan, and A. M. Tekalp, "High-resolution image reconstruction from a low-resolution image sequence in the presence of time-varying motion blur," in Proc. ICIP, Austin, TX, Nov. 1994, pp. 343-347.
26. _, "High-resolution standards conversion of low-resolution video," in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), Detroit, ML, May 1995, vol. II, pp. 2197-2200.
27. R. R. Schultz and R. L. Stevenson, "Improved definition video frame enhancement," IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), Detroit, MI., May 1995, vol. IV, pp. 2169-2172.
28. M. Elad and A. Feuer, "On restoration and super-resolution for continuous image sequence-adaptive filtering approach," Int. Rep. 942, The Techmon-Israel Inst. Technol., Haifa, Oct. 1994.
29. D. M. Young, Iterative Solution of Large Linear Systems. New York: Academic, 1971.
30. F. C. Schweppe, Uncertain Dynamic Systems. Englewood Cliffs, NJ: Prentice-Hall, 1973.
31. R. A. Horn and C. J. Johnson, Matrix Analysis. Cambridge, MA: Cambridge Univ. Press, 1985.
32. J. L. Brown, "Multi-channel sampling low-pass signals," IEEE Trans. Circuits Syst., vol. CAS-28, Feb. 1981.
33. M. Elad and A. Feuer, "Restoration of single super-resolution image from several blurred noisy and under-sampled measured images," Int. Rep. 973, The Technion, Israel Inst. Technol., Haifa, June 1995.