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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)693-706
Number of pages14
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number4
StatePublished - 2009


  • Image denoising
  • Image statistics
  • Markov random field

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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