Modeling multiscale subbands of photographic images with fields of Gaussian scale mixtures

S Lyu and E P Simoncelli

Published in IEEE Trans. Patt. Analysis and Machine Intelligence, vol.31(4), pp. 693--706, Apr 2009.
DOI: 10.1109/TPAMI.2008.107

This paper has been superseded by:
Nonlinear extraction of 'Independent Components' of natural images using radial Gaussianization
by S Lyu and E P Simoncelli.


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    • The local statistical properties of photographic images, when represented in a multi-scale basis, have been described using Gaussian scale mixtures. Here, we use this local description as a substrate for constructing a global field of Gaussian scale mixtures (FoGSMs). Specifically, we model multi-scale subbands as a product of an exponentiated homogeneous Gaussian Markov random field (hGMRF) and a second independent hGMRF. We show that parameter estimation for this model is feasible, and that samples drawn from a FoGSM model have marginal and joint statistics similar to subband coefficients of photographic images. We develop an algorithm for removing additive white Gaussian noise based on the FoGSM model, and demonstrate denoising performance comparable with state-of-the-art methods.
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