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Issue No.04 - April (2009 vol.31)
pp: 693-706
Siwei Lyu , University at Albany, State University of New York, Albany
Eero P. Simoncelli , Howard Hughes Medical Institute and New York University, New York
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.
INDEX TERMS
Image Representation, Statistical, Enhancement, Restoration
CITATION
Siwei Lyu, Eero P. Simoncelli, "Modeling Multiscale Subbands of Photographic Images with Fields of Gaussian Scale Mixtures", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.31, no. 4, pp. 693-706, April 2009, doi:10.1109/TPAMI.2008.107
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