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

Authors
Type
Published Article
Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
0162-8828
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Volume
31
Issue
4
Pages
693–706
Identifiers
DOI: 10.1109/TPAMI.2008.107
PMID: 19229084
Source
Medline

Abstract

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 Gaussian white noise based on the FoGSM model, and demonstrate denoising performance comparable with state-of-the-art methods.

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