Image denoising using mixtures of gaussian scale mixtures

Jose A. Guerrero-Colón, Eero P. Simoncelli, Javier Portilla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The local statistical properties of photographic images, when represented in a multi-scale basis, have been described using Gaussian scale mixtures (GSMs). In that model, each spatial neighborhood of coefficients is described as a Gaussian random vector modulated by a random hidden positive scaling variable. Here, we introduce a more powerful model in which neighborhoods of each subband are described as a finite mixture of GSMs. We develop methods to learn the mixing densities and covariance matrices associated with each of the GSM components from a single image, and show that this process naturally segments the image into regions of similar content. The model parameters can also be learned in the presence of additive Gaussian noise, and the resulting fitted model may be used as a prior for Bayesian noise removal. Simulations demonstrate this model substantially outperforms the original GSM model.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings
Pages565-568
Number of pages4
DOIs
StatePublished - 2008
Event2008 IEEE International Conference on Image Processing, ICIP 2008 - San Diego, CA, United States
Duration: Oct 12 2008Oct 15 2008

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2008 IEEE International Conference on Image Processing, ICIP 2008
CountryUnited States
CitySan Diego, CA
Period10/12/0810/15/08

Keywords

  • Gaussian scale mixture
  • Image denoising
  • Image modelling

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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