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Random cascades on wavelet trees and their use in analyzing and modeling natural images
Proc. SPIE 4119, 229 (2000); http://dx.doi.org/10.1117/12.408598
Monday 31 July 2000
San Diego, CA, USA
Wavelet Applications in Signal and Image Processing VIII
Akram Aldroubi, Andrew F. Laine, Michael A. Unser
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Add to FacebookWe develop a new class of non-Gaussian multiscale stochastic processes defined by random cascades on trees of wavelet or other multiresolution coefficients. These cascades reproduce a rich semi-parametric class of random variables known as Gaussian scale mixtures. We demonstrate that this model class can accurately capture the remarkably regular and non- Gaussian features of natural images in a parsimonious fashion, involving only a small set of parameters. In addition, this model structure leads to efficient algorithms for image processing. In particular, we develop a Newton- like algorithm for MAP estimation that exploits very fast algorithm for linear-Gaussian estimation on trees, and hence is efficient. On the basis of this MAP estimator, we develop and illustrate a denoising technique that is based on a global prior model, and preserves the structure of natural images.
© 2003 COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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Online May 20, 2003
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Martin J. Wainwright, Eero P. Simoncelli and Alan S. Willsky, "Random cascades on wavelet trees and their use in analyzing and modeling natural images",
Proc. SPIE 4119, 229 (2000); http://dx.doi.org/10.1117/12.408598
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