Variational Bayesian sparse kernel-based blind image deconvolution with student's-t priors

IEEE Transactions on Image Processing

Volumn 18, Issue 4, 2009, Pages 753-764

  Tzikas, Dimitris G ^a   Likas, Aristidis C ^a   Galatsanos, Nikolaos P ^b  

^a UNIVERSITY OF IOANNINA   (Greece)

^b UNIVERSITY OF PATRAS   (Greece)

Author keywords

Bayesian approach; Blind image deconvolution (BID); Inverse problem; Kernel model; Sparse prior; Student t distribution

Indexed keywords

BAYESIAN NETWORKS; CONVOLUTION; INFERENCE ENGINES; INVERSE PROBLEMS; NONLINEAR CONTROL SYSTEMS; OPTICAL TRANSFER FUNCTION; PROBABILITY DENSITY FUNCTION; PROBABILITY DISTRIBUTIONS; STUDENTS;

BAYESIAN APPROACH; BLIND IMAGE DECONVOLUTION (BID); KERNEL MODEL; SPARSE PRIOR; STUDENT-T DISTRIBUTION;

EDGE DETECTION;

EID: 63449135803     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2008.2011757     Document Type: Article

References (27)

1. D. Kundur and D. Hatzinakos, "Blind image deconvolution," IEEE Signal Process. Mag., vol. 13, no. 3, pp. 43-64, Mar. 1996.
2. D. Kundur and D. Hatzinakos, "Blind image deconvolution revisited," IEEE Signal Process. Mag., vol. 13, no. 6, pp. 61-63, Jun. 1996.
3. P. Campisi and K. Egiazarian, Eds., Blind Image Deconvolution: Theory and Applications CRC Boca Raton, FL, 2007.
4. T. F. Chan and C. K. Wong, "Total variation blind deconvolution," IEEE Trans. Image Process., vol. 7, no. 3, pp. 370-375, Mar. 1998.
5. T. F. Chan, S. Esedoglu, F. Park, and M. H. Yip, "Recent developments in total variation image restoration," in Handbook of Mathematical Models in Computer Vision. New York: Springer, 2005, vol. 3.
6. T. F. Chan, S. Esedoglu, F. Park, and M. H. Yip, Image Processing and Analysis - Variational, PDE, Wavelet, and Stochastic Methods. Philadelphia, PA: SIAM, 2005.
7. Y. L. You and M. Kaveh, "Blind image restoration by anisotropic regularization," IEEE Trans. Image Process., vol. 8, no. 3, pp. 396-407, Mar. 1999.
8. K. H. Yap, L. Guan, and W. Liu, "A recursive soft-decision approach to blind image deconvolution," IEEE Trans. Image Process., vol. 14, no. 5, pp. 624-633, May 2005.
9. L. Chen and K. H. Yap, "A soft double regularization approach to parametric blind image deconvolution," IEEE Trans. Image Process. vol. 14, no. 5, pp. 624-633, May 2005.
10. M. M. Bronstein, A. M. Bronstein, M. Zibulevsky, and Y. Zeevi, "Blind image deconvolution of images using optimal sparse representations," IEEE Trans. Image Process., vol. 14, no. 6, pp. 726-736, Jun. 2005.
11. B. D. Jeffs and J. C. Christou, D. Bonaccini and R. K. Tyson, Eds., "Blind Bayesian restoration of adaptive optics telescope images using generalized Gaussian markov random field models," in Proc. SPIE. Adaptive Optical System Technologies, 1998, vol. 3353, pp. 1006-1013.
12. N. P. Galatsanos, V. Z. Mesarovic, R. Molina, A. K. Katsaggelos, and J. Mateos, "Hyperparameter estimation in image restoration problems with partially-known blurs," Opt. Eng., vol. 41, no. 8, pp. 1845-1854, 2002.
13. J. Miskin and D. MacKay, Advances in Independent Component Analysis M. Girolami, Ed. New York: Springer-Verlag.
14. R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman, "Removing camera shake from a single photograph," ACM Trans. Graph. vol. 25, no. 3, pp. 787-794, 2006.
15. A. 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.
16. R. Molina, J. Mateos, and A. Katsaggelos, "Blind deconvolution using a variational approach to parameter, image, and blur estimation," IEEE Trans. Image Process., vol. 15, no. 12, pp. 3715-3727, Dec. 2006.
17. C. M. Bishop, Pattern Recognition and Machine Learning. New York: Springer, 2006.
18. M. Jordan, Z. Gharamani, T. Jaakkola, and L. Saul, "An introduction to variational methods for graphical models," in Learning in Graphical Models, M. Jordan, Ed. Boston, MA: Kluwer, 1998, pp. 105-162.
19. G. Chantas, N. P. Galatsanos, and A. Likas, "Bayesian restoration using a new nonstationary edge preserving image prior," IEEE Trans. Image Process., vol. 15, no. 10, pp. 2987-2997, Oct. 2006.
20. M. E. Tipping, "Sparse Bayesian learning and the relevance vector machine," J. Mach. Learn. Res., vol. 1, pp. 211-244, 2001.
21. M. A. T. Figueiredo, "Adaptive sparseness for supervised learning," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 9, pp. 1150-1159, Sep. 2003.
22. C. Liu and D. Rubin, "ML estimation of the t distribution using EM and its extensions, ECM and ECME," Statist. Sin., vol. 5, pp. 19-39, 1995.
23. D. Tzikas, A. Likas, and N. Galatsanos, "Large scale multikernel RVM for object detection," in Proc. Hellenic Conf. Artificial Intelligence, Heraklion, Greece, 2006, pp. 389-399.
24. G. Chantas, N. P. Galatsanos, and A. Likas, "Bayesian image restoration based on variational inference and a product of student-t priors," presented at the IEEE Int. Workshop MLSP, Thessaloniki, Greece, Aug. 2007.
25. M. Welling, G. E. Hinton, and S. Osindero, "Learning sparse topographic representations with products of student-t distributions," in Proc. Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2003, vol. 15.
26. J. Bernardo and A. Smith, Bayesian Theory. New York: Wiley, 1994.
27. Y. Yitzhaky, I. Mor, A. Lantzman, and N. S. Kopeika, "Direct method for restoration of motion-blurred images," J. Opt. Soc. Amer. A, vol. 15, pp. 1512-1519, Jun. 1998.