Image restoration using Gaussian scale mixtures in the wavelet domain

IEEE International Conference on Image Processing

Volumn 2, Issue , 2003, Pages 965-968

  Portilla, Javier ^a   Simoncelli, Eero ^b  

^a UNIVERSITY OF GRANADA   (Spain)

^b NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN ESTIMATOR; GAUSSIAN VECTOR;

GAUSSIAN NOISE (ELECTRONIC); SPURIOUS SIGNAL NOISE; STATISTICAL METHODS; VECTORS;

IMAGE RECONSTRUCTION;

EID: 0345565832     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. M J Wainwright and E P Simoncelli, "Scale mixtures of Gaussians and the statistics of natural images," in Adv. Neural Information Processing Systems, S. A. Solla, T. K. Leen, and K.-R. Müller, Eds., Cambridge, MA, May 2000, vol. 12, pp. 855-861, MIT Press.
2. M Wainwright, E Simoncelli, and A Willsky, "Random cascades on wavelet trees and their use in modeling and analyzing natural imagery," Applied and Computational Harmonic Analysis, 2000.
3. S M LoPresto, K Ramchandran, and M T Orchard, "Wavelet image coding based on a new generalized gaussian mixture model," in Data Compression Conf., Snowbird, Utah, 1997.
4. M S Crouse, R D Nowak, and R G Baraniuk, "Wavelet-based statistical signal processing using hidden Markov models," IEEE Trans. Signal Proc., vol. 46, pp. 886-902, April 1998.
5. M K Mihçak, I Kozintsev, K Ramchandran, and P Moulin, "Low-complexity image denoising based on statistical modeling of wavelet coefficients," IEEE Trans. Sig. Proc., vol. 6, no. 12, pp. 300-303, December 1999.
6. C Spence and L Parra, "Hierarchical image probability (HIP) model," in Adv. Neural Information Processing Systems, S. A. Solla, T. K. Leen, and K.-R. Müller, Eds., Cambridge, MA, May 2000, vol. 12, MIT Press.
7. J Portilla, V Strela, M Wainwright, and E Simoncelli, "Adaptive Wiener denoising using a Gaussian scale mixture model in the wavelet domain," in Proc 8th IEEE Int'l Conf on Image Proc, Thessaloniki, Greece, 2001, pp. 37-40
8. J Portilla, V Strela, M Wainwright, and E P Simoncelli, "Image denoising using scale mixtures of Gaussians in the wavelet domain,"IEEE Trans. Image Proc., In Press. 2003.
9. E P Simoncelli, W T Freeman, E H Adelson, and D J Heeger, "Shiftable multi-scale transforms," IEEE Trans Information Theory, vol. 38, no. 2, pp. 587-607, March 1992.
10. R R Coifman and D L Donoho, "Translation-invariant denoising," in Wavelets and statistics, A Antoniadis and G Oppenheim, Springer-Verlag lecture notes, San Diego, 1995.
11. E P Simoncelli, "Bayesian denoising of visual images in the wavelet domain," in Bayesian Inference in Wavelet Based Models, P Müller and B Vidakovic, Eds., chapter 18, pp. 291-308. Springer-Verlag, New York, Spring 1999.
12. J Starck, E J Candes, and D L Donoho, "The curvelet transform for image denoising," IEEE Trans. Image Proc., vol. 11, no. 6, pp. 670-684, June 2002.
13. D Andrews and C Mallows, "Scale mixtures of normal distributions", J. Royal Stat. Soc., vol. 36, pp. 99-, 1974.
14. E P Simoncelli, "Statistical models for images: Compression, restoration and synthesis," in 31st Asilomar Conf on Signals, Systems and Computers, Pacific Grove, CA, November 1997, pp. 673-678, IEEE Computer Society.
15. M Figueiredo and R Nowak, "Bayesian wavelet-based image estimation using non-informative priors," in SPIE Conf. on Mathematical Modeling, Bayesian Estimation, and Inverse Problems, Denver CO, Jul 1999.
16. JO Berger, "Prior information and subjective probability," in Statistical Decission Theory and Bayesian Analysis, Springer Series in Statistics. Springer-Verlag, 1990.