Using intensity edges to improve parameter estimation in blind image restoration

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 3459, Issue , 1998, Pages 73-81

  Tonazzini, Anna ^a   Bedini, Luigi ^a  

^a ISTI CNR   (Italy)

Author keywords

Blind image restoration; Edge preserving regularization; Markov random fields; Saddle point approximation; Unsupervised image restoration

Indexed keywords

BAYESIAN NETWORKS; CURVE FITTING; DEGRADATION; DIFFERENTIAL EQUATIONS; HIDDEN MARKOV MODELS; IMAGE RECONSTRUCTION; IMAGE SEGMENTATION; INFERENCE ENGINES; INVERSE PROBLEMS; OPTOELECTRONIC DEVICES; PARAMETER ESTIMATION; POLYNOMIAL APPROXIMATION; REPAIR; RESTORATION;

BLIND IMAGE RESTORATION; EDGE-PRESERVING REGULARIZATION; MARKOV RANDOM FIELDS; SADDLE POINT APPROXIMATION; UNSUPERVISED IMAGE RESTORATION;

EDGE DETECTION;

EID: 34247465900     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.323821     Document Type: Conference Paper

References (31)

1. E. Aarts and J. Korst, Simulated Annealing and Boltzmann Machines, John Wiley & Sons, 1989.
2. G. R. Ayers and J. G. Dainty, "Iterative blind deconvolution method and its applications, Opt. Lett. 13, pp. 547-549, 1988.
3. L. Bedini and A. Tonazzini, "Image restoration preserving discontinuities: the Bayesian approach and neural networks, Image and Vision Computing 10, pp. 108-118, 1992.
4. L. Bedini, I. Gerace, and A. Tonazzini, 'A deterministic algorithm for reconstructing images with interacting discontinuities', CVGIP: Graphical Models and Image Processing 56, pp. 1 09- 123, 1994.
5. L. Bedini, E. Salerno and A. Tonazzini, Edge-preserving tomographic reconstruction from Gaussian data using a Gibbs prior and a Generalized Expectation-Maximization algorithm, Int. Journal of Imaging Systems and Technology 5, pp. 231-238, 1994.
6. L. Bedini. I. Gerace, E. Salerno and A. Tonazzini, "Models and algorithms for edge-preserving image reconstruction, Advances in Imaging and Electron Physics 97, P.W. Hawkes Ed., pp. 86-189, 1996.
7. L. Bedini, A. Tonazzini and S. Minutoli, "A neural architecture for simultaneous MAP image restoration and ML estimation of edge-preserving Gibbs priors, Techn. Report B4-48, IEI-CNR, 1996.
8. J. Besag, "On the statistical analysis of dirty pictures", J. Royal Statist. Soc. B48, pp. 259-302, 1986.
9. A. Blake and A. Zisserman, Visual Reconstruction , MIT Press, Cambridge, MA, 1987.
10. B. L. K. Davey, R. G. Lane and R. H. T. Bates, "Blind deconvolution of noisy complex-valued images", Optics Commun. 69, pp. 353-356, 1989.
11. A. P. Dempster, N. M. Laird and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm", J. Roy. Statist. Soc. B39, pp. 1-38, 1977.
12. X. Descombes, R. Morris, J Zerubia and M. Berthod, "Maximum likelihood estimation of Markov random field parameters using Markov chain Monte Carlo algorithms", in Lecture Notes in Computer Science 1223, M. Pelillo and E. R. Hancock Eds., pp. 133-148, 1997.
13. S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images, IEEE Trans. Pattern Anal. Machine Intell.. PAMI-6, pp. 721-740, 1984.
14. B. Gidas, "Parameter estimation for Gibbs distributions from fully observed data", in Markov Random Fields: theory and applications, R. Chellappa and A. Jam Eds., Academic Press, 1993.
15. D. M. Higdon, V. E. Johnson, T. G. Turkington, J. E. Bowsher, D.R. Gilland and R.J. Jaszczak, "Fully Bayesian estimation of Gibbs hyperparameters for emission computed tomography data", Techn. Report 96-21, Institute of Statistics and Decision Sciences, Duke University, 1995.
16. G. E. Hinton, T. J. Sejnowski and D. H. Hackley, "Boltzmann machine: constraint satisfaction networks that learn", Techn. Report CNU-CS-84-119, Carnegie-Mellon University, 1984.
17. J. J. Hopfield, "Neurons with graded response have collective computational properties like those of two-state neurons", Proc. Natl. Acad. Sci. USA 81, pp. 3088-3092, 1984.
18. F. C. Jeng and J. W. Woods, "Compound Gauss-Markov random fields for image estimation", IEEE Trans. Signal Processing SP-39, pp. 683-697, 1991.
19. A. K. Katsaggelos and K. T. Lay, "Maximum likelihood blur identification and image restoration using the EM algorithm", IEEE Trans. Signal Processing SP-39, pp. 729-732, 1991.
20. R. L. Lagendijk, A. M. Tekalp and J. Biemond, "Maximum likelihood image and blur identification: a unifying approach", Opt. Eng. 29, pp. 422-435, 1990.
21. R. L. Lagendijk, J. Biemond and D. E. Boekee, "Identification and restoration of noisy blurred images using the expectation-maximization algorithm", IEEE Acoust. Speech Signal Proc. ASSP-38, pp. 1180-1191, 1990.
22. S. Lakshmanan and H. Derin, "Simultaneous parameter estimation and segmentation of Gibbs random fields using simulated annealing, IEEE Trans. Pattern Anal. Machine Intell, PAMI-11, pp. 799-813, 1989.
23. B. C. McCallum, "Blind deconvolution by simulated annealing", Optics Commun. 75, pp. 101-105, 1990.
24. J. Marroquin, S. Mitter and T. Poggio, "Probabilistic solution of ill-posed problems in computational vision", J. Am. Stat. Ass. 82, pp. 76-89, 1987.
25. S. I. Reeves and R. M. Mersereau, "Blur identification by the method of generalized cross-validation", IEEE Trans. Image Processing IP-1, pp. 301-311, 1992.
26. A. M. Tekalp and H. Kaufman, "On statistical identification of a class of linear space-invariant blurs using nonminimum-phase arma models", IEEE Trans. Acoust. Speech Signal Proc. ASSP-38, pp. 1360-1363, 1988.
27. A. Tonazzini, L. Bedini and S. Minutoli, "Joint MAP image restoration and ML parameter estimation using MRF models with explicit lines", Proc. of the IASTED Int. Conf. Signal and Image Processing (SIP'97), pp. 215-220, 1997.
28. Y. Yang, N. P. Galatsanos and H. Stark, "Projection- based blind deconvolution", J. Opt. Soc. Amer. A11, pp. 2401-2409, 1994.
29. Y. You and M. Kaveh, "A regularization approach to joint blur identification and image restoration", IEEE Trans. Image Processing IP-5. pp. 416-428, 1996.
30. J. Zhang, "The mean field theory in EM procedures for Markov Random Fields", IEEE Trans. Signal Processing SP-40, pp. 2570-2583, 1992.
31. J. Zhang, "The mean field theory in EM procedures for blind Markov Random Field image restoration", IEEE Trans. Image Proc. IP-2, pp. 27-40, 1993.