Blind deconvolution using generalized cross-validation approach to regularization parameter estimation

IEEE Transactions on Image Processing

Volumn 20, Issue 3, 2011, Pages 670-680

  Liao, Haiyong ^a   Ng, Michael K ^a  

^a HONG KONG BAPTIST UNIVERSITY   (Hong Kong)

Author keywords

Alternating minimization; blind deconvolution; generalized cross validation (GCV); regularization parameters; total variation (TV)

Indexed keywords

ALTERNATING MINIMIZATION; BLIND DECONVOLUTION; GENERALIZED CROSS VALIDATION; REGULARIZATION PARAMETERS; TOTAL VARIATION;

ALGORITHMS; CONVOLUTION; OPTIMIZATION; PARAMETER ESTIMATION; PARAMETERIZATION;

DECONVOLUTION;

EID: 79951838700     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2010.2073474     Document Type: Article

References (40)

1. D. Kundur and D. Hatzinakos, "A novel blind deconvolution scheme for image restoration using recursive filtering," IEEE Trans. Signal Process., vol. 46, no. 2, pp. 375-390, Feb. 1998. (Pubitemid 128744989)
2. M. Ng, R. Plemmons, and S. Qiao, "Regularization of RIF blind image deconvolution," IEEE Trans. Image Process., vol. 9, no. 6, pp. 1130-1134,Jun.2000.
3. C. Ong and J. Chambers, "An enhanced NAS-RIF algorithm for blind image deconvolution," IEEE Trans. Image Process., vol. 8, no. 7, pp. 982-992, Jul. 1999.
4. A. Katsaggelos and K. Lay, "Simltaneous blur identification and image restoration using the EM algorithm," in Proc. SPIE Conf, Visual Commun. ImageProcess. IV, 1989, vol. 1199, pp. 1474-1485.
5. R. Lagendijk, A. Tekalp, and J. Bidmond, "Maximum likelihood image and blur identification: A unifying approach," Opt. Eng., vol. 29, pp. 422-435, 1990. (Pubitemid 20717536)
6. A. C. Likas and N. P. Galatsanos, "A variational approach for Bayesian blind image deconvolution," IEEE Trans. Signal Process., vol. 52,no. 8, pp. 2222-2233, Aug. 2004.
7. R. Molina, J. Mateos, and A. K. Katsaggelos, "Blind deconvolution using a variational approach to parameter, image, and blur estimation," IEEE Trans.ImageProcess.,vol. 15,no. 12,pp.3715-3727,Dec.2006. (Pubitemid 44811684)
8. D. Tzikas, A. Likas, and N. Galatsanos, "Variational Bayesian sparse kernel-based blind image deconvolution with student's-t priors," IEEE Trans. Image Process., vol. 18, no. 4, pp. 753-764, Apr. 2009.
9. S. Reeves and R. Mersereau, "Blur identification by the method of generalizedcrossvalidation,"IEEE Trans. ImageProcess., vol. 1, no.3,pp. 301-311,Jul. 1992.
10. J. W. Miskin and D. J. C. MacKay, "Ensemble learning for blind image separation and deconvolution," in Advances in Independent Component Analysis, M. Girolami, Ed. New York: Springer-Verlag, 2000.
11. K. Z. Adami, "Variational methods in Bayesian deconvolution," in Proc. PHYSTAT2003, SLAC, Stanford, CA, 2003, pp. 8-11.
12. T. G. Stockham, T. M. Cannon, and R. B. Ingebretsen, "Blind deconvolution through digital signal processing," Proc. IEEE, vol. 63, no. 4, pp. 678-692, Apr. 1975.
13. M. Cannon, "Blind deconvolution of spatially invariant image blurs with phase," IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-24, no. 1, pp. 58-63, Jan. 1976. (Pubitemid 8037997)
14. R. G. Lane and R. H. T. Bates, "Automatic multidimensional deconvolution," J. Opt. Soc. Amer. A, vol. 4, no. 1, pp. 180-188, 1987.
15. R. Nakagaki and A. K. Katsaggelos, "A VQ-based blind image restoration algorithm," IEEE Trans. Image Process., vol. 12, no. 9, pp. 1044-1053, Sep. 2003.
16. K. Panchapakesan, D. G. Sheppard, M. W. Marcellin, and B. R. Hunt, "Blur identification from vector quantizer encoder distortion," IEEE Trans. Image Process., vol. 10, no. 3, pp. 465-470, Mar. 2001. (Pubitemid 32286650)
17. Y. You and M. Kaveh, "A regularization approach to joint blur identification and image restoration," IEEE Trans. Image Process., vol. 5, no. 3, pp. 416-427, Mar. 1996.
18. T. F. Chan and C. K. Wong, "Total variation blind deconvolution," IEEE Trans. Image Process., vol. 7, no. 3, pp. 370-375, Mar. 1998. (Pubitemid 128745355)
19. S. D. Babacan, R. Molina, and A. K. Katsaggelos, "Variational Bayesian blind deconvolution using a total variation prior," IEEE Trans. Image Process., vol. 18, no. 1, pp. 12-26, Jan. 2009.
20. Y. You and M. Kaveh, "Blind image restoration by anisotropic regularization," IEEE Trans. Image Process., vol. 8, no. 3, pp. 396-407, Aug. 1999.
21. Y. Huang and M. Ng, "Lipschitz and total-variational regularization for blind deconvolution," Commun. Comput. Phys., vol. 4, pp. 195-206, Feb. 2008.
22. H. Liao, F. Li, and M. Ng, "Selection of regularization parameter in total variation image restoration," J. Opt. Soc. Amer. A, vol. 26, pp. 2310-2320, Nov. 2009.
23. Y. Yang, N. Galatsanos, and H. Stark, "Projection based blind deconvolution," J. Opt. Soc. Amer. A, vol. 11, no. 9, pp. 2401-2409, 1994.
24. D. Kundur and D. Hatzinakos, "Blind image deconvolution," IEEE Signal Process. Mag., vol. 13, no. 3, pp. 43-64, Mar. 1996. (Pubitemid 126518975)
25. P. Campisi and K. Egiazarian, Blind Image Deconvolution: Theory and Applications. Boca Raton, FL: CRC, 2007.
26. L. Rudin, S. Osher, and E. Fatemi, "Nonlinear total variation based noise removal algorithms," Physica D, vol. 60, pp. 259-268, 1992.
27. T. Chan and K. Chen, "An optimization-based multilevel algorithm for total variation image denoising," SIAMJ. Multi. Model. Simul., vol. 5, pp. 615-645, 2006. (Pubitemid 46901175)
28. Y. Huang, M. Ng, and Y. Wen, "A fast total variation minimization method for image restoration," SIAM J. Multiscale Model. Simul., vol. 7, pp. 774-795, 2008.
29. X. Guo, F. Li, and M. Ng, "A fast-TV algorithm for image restoration," SIAM J. Sci. Comput., vol. 31, pp. 2322-2341, 2009.
30. Y. Wang, J. Yang, W. Yin, and Y. Zhang, "A new alternating minimization algorithm for total variation image reconstruction," SIAM J. Imaging Sci., vol. 1, pp. 248-272, 2008.
31. J. Yang, W. Yin, Y. Zhang, and Y. Wang, "A fast algorithm for edgepreserving variational multichannel image restoration," CAAM, Rice Univ., Houston, TX, Tech. Rep. 08-09
32. M.Ng,Iterative Methods for Toeplitz Systems. Oxford,U.K.:Oxford Univ. Press, 2004.
33. A. Chambolle, "An algorithm for total variation minimization and applications," J. Math. Imaging Vis., vol. 20, pp. 89-97, 2004.
34. M. Ng, L. Qi, Y. Yang, and Y. Huang, "On semismooth Newton's methods for total variation minimization," J. Math. Imaging Vis., vol. 27, pp. 265-276, 2007. (Pubitemid 46664095)
35. G. Golub and U. V. Matt, "Generalized cross-validation for large scale problems,"J. Comput. Graph. Stat, vol. 6, pp. 1-34, 1997.
36. G. Golub, M. Heath, and G. Wahba, "Generalized cross-validation as a method for choosing a good ridge parameter," Technometrics, vol. 21, pp. 215-223, 1979.
37. M. Kilmer and D. O'leary, "Choosing regularization parameters in iterative methods for ill-posed problems," SIAM. J. MatrixAnal. & Appl., vol. 22, pp. 1204-1221, 2001.
38. G. Golub and G. Meurant, "Matrices, moments and quadrature," in Numerical Analysis, D. Griffiths and G. Watson, Eds. Essex, U.K.: Longman, 1994, pp. 105-156.
39. G. Forsythe, M. Malcolm, and C. Moler, Computer Methods for Mathematical Computations. Englewood Cliffs, NJ: Prentice-Hall, 1976.
40. R. Brent, Algorithms for Minimization Without Derivatives. Englewood Cliffs: Prentice-Hall, 1973.