1. DESCRIPTION
1.1. Introduction
1.2. Image Statistics
1.3. GSM
1.4. Plus Noise
1.5. BLS-GSM
1.6. Results
1.7. References
2. EXAMPLES
3. TEST IMAGES
4. SOFTWARE
D Andrews and C Mallows
Scale mixtures of normal distributions
J. Royal Stat. Soc., vol. 36, pp. 99{, 1974.
R W Buccigrossi and E P Simoncelli
Progressive wavelet image coding based on a conditional probability model
Proc. Int'l Conf. Acoustics Speech and Signal Processing Munich, Germany. April 21-24, 1997.
M. S. Crouse, R. D. Nowak, and R. G. Baraniuk
Wavelet-based statistical signal processing using hidden Markov models
IEEE Trans. Signal Processing, vol. 46, pp. 886–902, Apr. 1998.
S G Chang, B Yu, and M Vetterli
Spatially adaptive wavelet thresholding with context modeling for image denoising
IEEE Int'l Conf. on Image Proc., Chicago, October 1998.
D J Field
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J S Lee
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X Li and M T Orchard
Spatially adaptive image denoising under overcomplete expansion
IEEE Int'l Conf on Image Proc. Vancouver, September 2000.
S G Mallat
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IEEE Pat. Anal. Mach. Intell.
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M K Mihcak, 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.
J Portilla, V Strela, M Wainwright, E P Simoncelli.
Adaptive Wiener Denoising using a Gaussian Scale Mixture Model in the Wavelet Domain.
Abstract / Reprint (111k, ps.gz) / Reprint (218k, pdf) / Presentation (2.3Mb, pdf)
J Portilla, V Strela, M Wainwright, E P Simoncelli.
Image Denoising using Gaussian Scale Mixtures in the Wavelet Domain
Technical Report TR2002-831, Computer Science Department, Courant
Institute of Mathematical Sciences, New York University. September 2002.
J Portilla, E P Simoncelli
Image Restoration using Gaussian Scale Mixtures in the Wavelet Domain.
Abstract / Reprint (124k, pdf) / Poster (shrunk) (1.1Mb, pdf)
J Portilla, V Strela, M Wainwright, E P Simoncelli.
Image Denoising using Scale Mixtures of Gaussians in the Wavelet Domain.
Abstract / Preprint (300 Kb, pdf)
J. Portilla
Blind Non-White Noise Removal in Images using Gaussian Scale Mixtures in the Wavelet Domain
Proc. of the
4th IEEE Benelux Signal Proc. Symposium, Hilvarenbeek, The Netherlands, pp. 17-20, April 2004Abstract / Reprint (272 Kb, pdf)
J. Portilla
Full Blind Denoising through Noise Covariance Estimation using Gaussian Scale Mixtures in the Wavelet Domain
L Sendur and I W Selesnick
Bivariate shrinkage with local variance estimation
IEEE Signal Processing Letters, vol. 9, no. 12, pp. 438-441. December 2002.
J. M. Shapiro
Embedded image coding using zerotrees of wavelet coefficients
IEEE Trans. Signal Processing, vol. 41, no. 12, pp. 3445-3462. December 1993.
E P Simoncelli and W T Freeman Available on line
The steerable
pyramid: A flexible architecture for multi-scale derivative computation.
Proc 2nd IEEE Int'l Conf on Image Processing, Washington,
DC. Oct 1995.
E P Simoncelli and E H Adelson. Available on line
Noise removal via Bayesian wavelet coring
Third Int’l Conf on Image Proc, Lausanne, Sep 1996, vol. I, pp. 379–382
E. P. Simoncelli. Available on line
Statistical models for images: Compression, restoration and synthesis
Proc. 31st Asilomar Conf. on Signals, Systems and Computers
E. P. Simoncelli. Available on line
Bayesian denoising of visual images in the wavelet domain
in Bayesian Inference in Wavelet Based Models, ch 18, pp. 291–308.
Springer-Verlag, Lecture Notes in Statistics, vol. 141. 1999.
V. Strela
Denoising via block Wiener filtering in wavelet domain
Proc. 3rd Eur. Congr. Math., Barcelona, Spain, July 2000.
J L Starck, D L Donoho, and E Candes
Very high quality image restoration
Proc. SPIE Conf. Signal and Image Processing. San Diego, August 2001, vol. 4478, pp. 9-19.
M J Wainwright and E P Simoncelli Available on line
Scale mixtures of Gaussians and the statistics of natural images
Adv. Neural Information Processing Systems, S. A. Solla, T. K. Leen, and K.-R. Muller, Eds., Cambridge, MA, May 2000, vol. 12, pp. 855-861, MIT Press.
M J Wainwright, E P Simoncelli, and A S Willsky Available on line
Random cascades on wavelet trees and their use in modeling and analyzing natural imagery
Applied and Computational Harmonic Analysis, vol. 11, no. 1, pp. 89-123, July 2001.