Mutual information regularized bayesian framework for multiple image restoration

Proceedings of the IEEE International Conference on Computer Vision

Volumn I, Issue , 2005, Pages 190-197

  Chen, Yunqiang ^a   Wang, Hongcheng ^a   Fang, Tong ^a   Tyan, Jason ^a  

^a SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN FRAMEWORK; FRAMEWORK GENERALIZATION; MUTUAL INFORMATION (MI); TAYLOR EXPANSION;

APPROXIMATION THEORY; COMPUTER VISION; ERROR ANALYSIS; IMAGING TECHNIQUES; MATHEMATICAL MODELS; ROBUSTNESS (CONTROL SYSTEMS);

IMAGE RECONSTRUCTION;

EID: 33745957036     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2005.164     Document Type: Conference Paper

References (20)

1. M. Belge, M. Kilmer, and E. Miller. Wavelet domain image restoration with adaptive edge-preserving regularization. IEEE Trans. Image Processing, 9(4):598-608, 2000.
2. Y. Chen, H. Wang, T. Fang, and J. Tyan. Image compounding based on independent noise constraint. In Proc. IEEE Int'l Conf. Acoustic Speech Signal Processing, March 2005.
3. D. Edwards. Introduction to Graphical Modelling, 2nd ed. Springer-Verlag, 2000.
4. S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Machine Intell., 6(6):721-741, November 1984.
5. H. Ishikawa. Exact optimization for markov random fields with convex priors. IEEE Trans. Pattern Anal. Machine Intell., 20(10):1333-1336, October 2003.
6. A. C. Kak and M. Slaney. Principles of Computerized Tomographic Imaging. Society of Industrial and Applied Mathematics, 2001.
7. J. Kim, V. Kolmogorov, and R. Zabih. Visual correspondence using energy minimization and mutual information. In Proc. IEEE Int'l Conf. on Computer Vision, 2003.
8. T. W. Lee. Independent Component Analysis: Theory and Applications. Kluwer Academic Publishers, 1998.
9. S.Z. Li. Markov Random Field Modeling in Image Analysis. Tokyo: Springer, 2nd edition, 2001.
10. J. Mateos, R. Molina, and A. Katsaggelos. Bayesian high resolution image reconstruction with incomplete multisensor low resolution systems. In IEEE Int'l Conf. on Acoustic, Speech and Signal Processing, pages 111:705-708, 2003.
11. R. Molina, J. Mateos, A. K. Katsaggelos, and M. Vega. Bayesian multichannel image restoration using compound Gauss-Markov random fields. IEEE Trans. Image Processing, 1212:1642-1654, December 2003.
12. D. D. Muresan and T. W. Parks. Adaptively quadratic (AQua) image interpolation. IEEE Trans. Image Processing, 24:657-673, 2002.
13. L. R. Rabiner and B. H. Juang. An introduction to hidden Markov models. IEEE Trans. Acoust., Speech, Signal Processing, 3(1):4-15, January 1986.
14. P. M. Shankar. Speckle reduction in ultrasound b-scans using weighted averaging in spatial compounding. IEEE Trans. on Ultrasonics, Ferroelectrics and Frequency Control, 33(6):754-758, 1986.
15. G. E. Trahey, J. W. Allison, S. W. Smith, and O. T. von Ramm. A quantitative approach to speckle reduction via frequency compounding. Ultrasonic Imaging, 8:151-164, 1986.
16. V. Vapnik. The Nature of Statistical Learning Theory. New York, NY: Springer-Verlag, 1995.
17. R. F. Wagner, S. W. Smith, J. M. Sandrik, and H. Lopez. Statistics of speckle in ultrasound B-scans. IEEE Trans. on Sonics and Ultrasonics, 33(3): 156-163, 1983.
18. W. F. Walker and G. E. Trahey. The application of k-space in medical ultrasound. IEEE Trans. on Ultrasonics, Ferroelectrics and Frequency Control, 45:541-558, 1998.
19. Y. Weiss. Smoothness in layers: Motion segmentation using nonparametric mixture estimation. In Proc. IEEE Int'l Conf. on Comput. Vis. and Patt. Recog., pages 520-527, 1997.
20. J. Zhang and G. G. Hanauer. The application of mean field theory to image motion estimation. IEEE Trans. Image Processing, 4(1): 19-33, January 1995.