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Proceedings Paper

Image denoising using a local Gaussian scale mixture model in the wavelet domain
Author(s): Vasily Strela; Javier Portilla; Eero P. Simoncelli
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Paper Abstract

The statistics of photographic images, when decomposed in a multiscale wavelet basis, exhibit striking non-Gaussian behaviors. The joint densities of clusters of wavelet coefficients are well-described as a Gaussian scale mixture: a jointly Gaussian vector multiplied by a hidden scaling variable. We develop a maximum likelihood solution for estimating the hidden variable from an observation of the cluster of coefficients contaminated by additive Gaussian noise. The estimated hidden variable is then used to estimate the original noise-free coefficients. We demonstrate the power of this model through numerical simulations of image denoising.

Paper Details

Date Published: 4 December 2000
PDF: 9 pages
Proc. SPIE 4119, Wavelet Applications in Signal and Image Processing VIII, (4 December 2000); doi: 10.1117/12.408621
Show Author Affiliations
Vasily Strela, Drexel Univ. (United States)
Javier Portilla, New York Univ. (United States)
Eero P. Simoncelli, New York Univ. (United States)

Published in SPIE Proceedings Vol. 4119:
Wavelet Applications in Signal and Image Processing VIII
Akram Aldroubi; Andrew F. Laine; Michael A. Unser, Editor(s)

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