Image denoising using a local Gaussian scale mixture model in the wavelet domain

V Strela, J Portilla and E P Simoncelli

Published in Proc. SPIE, Conf. on Wavelet Applications in Signal and Image Processing, VIII, vol.4119 pp. 363--371, Jul 2000.
© SPIE - the International Society for Optical Engineering, 2000

DOI: 10.1117/12.408621

This paper has been superseded by:
Image denoising using scale mixtures of Gaussians in the wavelet domain
J Portilla, V Strela, M J Wainwright and E P Simoncelli.
IEEE Trans. Image Processing, vol.12(11), pp. 1338--1351, Nov 2003.
Recipient, IEEE Signal Processing Society Best Paper Award, 2008.


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    • 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 (corresponding to basis functions at nearby spatial positions, orientations and scales) 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.
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