A Markov model for blind image separation by a mean-field EM algorithm

IEEE Transactions on Image Processing

Volumn 15, Issue 2, 2006, Pages 473-482

  Tonazzini, Anna ^a   Bedini, Luigi ^a   Salerno, Emanuele ^a  

^a ISTI CNR   (Italy)

Author keywords

Blind source separation (BSS); Edge and feature detection; Markov random fields (MRFs); Parameter learning; Scene analysis

Indexed keywords

ALGORITHMS; EDGE DETECTION; IMAGE RECONSTRUCTION; INDEPENDENT COMPONENT ANALYSIS; MARKOV PROCESSES; MATHEMATICAL MODELS; OPTIMIZATION; PARAMETER ESTIMATION; PROBABILITY;

MARKOV RANDOM FIELDS (MRFS); PARAMETER LEARNING; SCENE ANALYSIS;

BLIND SOURCE SEPARATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; EVALUATION; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; INFORMATION RETRIEVAL; METHODOLOGY; PROBABILITY; SIGNAL PROCESSING; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; MARKOV CHAINS; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; SIGNAL PROCESSING, COMPUTER-ASSISTED; SUBTRACTION TECHNIQUE;

EID: 31144447882     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2005.860323     Document Type: Article

References (35)

1. A. Cichocki and S. Amari, Adaptive Blind Signal and Image Processing. New York: Wiley, 2002.
2. M. Burger and O. Scherzer, "Regularization methods for blind deconvolution and blind source separation," Math. Control Signals Syst., vol. 14, pp. 358-383, 2001.
3. S. Geman and D.Geman, "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images," IEEE Trans. Pattern Anal. Mach. Intell., vol. 6, no. 6, pp. 721-740, Jun. 1984.
4. J. Marroquin, S. Mitter, and T. Poggio, "Probabilistic solutions of ill-posed problems in computational vision," J. Amer. Stat. Assoc., vol. 82, pp. 76-89, 1987.
5. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum Likelihood from incomplete data via the EM algorithm," J. Roy. Stat. Soc. B, vol. 39, pp. 1-38, 1977.
6. A. J. Bell and T. J. Sejnowski, "An information maximization approach to blind separation and blind deconvolution," Neural Comput., vol. 7, pp. 1129-1159, 1995.
7. T. Lee, M. Lewicki, and T. Sejnowski, "Independent component analysis using an extended infomax algorithm for mixed sub-gaussian and super-gaussian sources," Neural Comput., vol. 11, pp. 409-433, 1999.
8. J.-F. Cardoso, "High-order contrasts for independent component analysis," Neural Comput., vol. 11, pp. 157-192, 1999.
9. S. Amari and A. Cichocki, "Adaptive blind signal processing - neural network approaches," Proc. IEEE, vol. 86, pp. 2026-2048, 1998.
10. A. Hyvärinen, "Fast and robust fixed-point algorithms for independent component analysis," IEEE Trans. Neural Netw., vol. 10, no. 3, pp. 626-634, Jun. 1999.
11. A. Hyvärinen, "Gaussian moments for noisy independent component analysis," IEEE Signal Process. Lett., vol. 6, no. 2, pp. 145-147, Feb. 1999.
12. H. Attias, "Independent factor analysis," Neural Comput., vol. 11, pp. 803-851, 1999.
13. E. Moulines, J. F. Cardoso, and E. Gassiat, "Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models," in Proc. Int. Conf. Acoustics, Speech, Signal Processing, vol. 5, 1997, pp. 3617-3620.
14. E. Kuruoglu, L. Bedini, M. T. Paratore, E. Salerno, and A. Tonazzini, "Source separation in astrophysical maps using independent factor analysis," Neural Netw., vol. 16, pp. 479-491, 2003.
15. K. Knuth, "Bayesian source separation and localization," in Proc. SPIE Bayesian Inference for Inverse Problems, 1998, pp. 147-158.
16. S. E. Lee and S. J. Press, "Robustness of Bayesian factor analysis estimates," Commun. Stat. - Theory Meth., vol. 27, pp. 1871-1893, 1998.
17. A. Mohammad-Djafari, "A Bayesian approach to source separation," presented at the 19th Int. Workshop on Maximum Entropy and Bayesian Methods, 1999.
18. A. Tonazzini, L. Bedini, E. E. Kuruoglu, and E. Salerno, "Blind Separation of Time-Correlated Sources From Noisy Data," Tech. Rep. IEI-CNR TR-42-2001, 2001.
19. L. Bedini, I. Gerace, E. Salerno, and A. Tonazzini, "Models and algorithms for edge-preserving image reconstruction," in Advances in Imaging and Electron Physics, P. W. Hawkes, Ed. New York: Academic, 1996, vol. 97, pp. 86-189.
20. S. Z. Li, "On discontinuity-adaptive smoothness priors in computer vision," IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 4, pp. 576-586, Apr. 1995.
21. S. Z. Li, "Close-form solution and parameter selection for convex minimization-based edge-preserving smoothing," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 8, pp. 916-932, Aug. 1998.
22. L. Bedini, I. Gerace, and A. Tonazzini, "A GNC algorithm for constrained image reconstruction with continuous-valued line processes," Pattern Recognit. Lett., vol. 15, pp. 907-918, 1994.
23. D. Shulman and J. Y. Herve, "Regularization of discontinuous flow fields," in Proc. Workshop Vis. Motion, 1989, pp. 81-86.
24. L. Bedini, E. Salerno, and A. Tonazzini, "Blind source separation from noisy data using Bayesian estimation and Gibbs priors," in Proc. IASTED Sig. Image Processing Conf., 2001, pp. 108-112.
25. D. Chandler, Introduction to Modern Statistical Mechanics. Oxford, U.K.: Oxford Univ. Press, 1987.
26. J. Zhang, "The mean field theory in EM procedures for Markov random fields," IEEE Trans. Signal Process., vol. 40, no. 11, pp. 2570-2583, Nov. 1992.
27. J. Zhang, "The mean field theory in EM procedures for blind Markov random field image restoration," IEEE Trans. Image Process., vol. 2, no. 1, pp. 27-40, Jan. 1993.
28. A. Tonazzini, L. Bedini, E. E. Kuruoglu, and E. Salerno, "Blind separation of auto-correlated images from noisy mixtures using MRF models," in Proc. ICA, Nara, Japan, Apr. 2003, pp. 675-680.
29. D. Geiger and F. Girosi, "Parallel and deterministic algorithms from MRF's: Surface reconstruction," IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 5, pp. 401-412, May 1991.
30. A. Tonazzini, L. Bedini, and E. Salerno, "Independent component analysis for document restoration," Int. J. Doc. Anal. Recognit., vol. 7, pp. 17-27, Mar. 2004.
31. R. L. Easton. Simulating Digital Image Processing used for the Archimedes Palimpsest. [Online]. Available: http://www.cis.rit.edu/ people/faculty/easton/k-12/index.htm
32. A. Blake and A. Zisserman, Usual Reconstruction. Cambridge, MA: MIT Press, 1987.
33. D. Mumford and J. Shah, "Optimal approximations by piecewise smooth functions and associated variational problems," Commun. Pure Appl. Math., vol. 42, no. 5, pp. 577-685, 1989.
34. J.-M. Morel and S. Solimini, "Variational methods in image segmentation with 7 image processing experiments," in Progress in Nonlinear Differential Equations and Their Applications. Basel, Switzerland: Birkhäuser, 1994, vol. 14.
35. A. Tonazzini and L. Bedini, "Degradation identification and model parameter estimation in discontinuity-adaptive visual reconstruction," Adv. Imag. Electron. Phys., vol. 120, pp. 193-284, 2002.