Image modeling and denoising with orientation-adapted Gaussian scale mixtures

David K. Hammond, Eero P. Simoncelli

Research output: Contribution to journalArticlepeer-review


We develop a statistical model to describe the spatially varying behavior of local neighborhoods of coefficients in a multiscale image representation. Neighborhoods are modeled as samples of a multivariate Gaussian density that are modulated and rotated according to the values of two hidden random variables, thus allowing the model to adapt to the local amplitude and orientation of the signal. A third hidden variable selects between this oriented process and a nonoriented scale mixture of Gaussians process, thus providing adaptability to the local orientedness of the signal. Based on this model, we develop an optimal Bayesian least squares estimator for denoising images and show through simulations that the resulting method exhibits significant improvement over previously published results obtained with Gaussian scale mixtures.

Original languageEnglish (US)
Pages (from-to)2089-2101
Number of pages13
JournalIEEE Transactions on Image Processing
Issue number11
StatePublished - 2008


  • Gaussian Scale Mixtures
  • Image denoising
  • Image processing
  • Statistical image modeling
  • Wavelet transforms

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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