Image denoising with an orientation-adaptive Gaussian scale mixture model

David K. Hammond, Eero P. Simoncelli

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

Abstract

We develop a statistical model for images that explicitly captures variations in local orientation and contrast. Patches of wavelet coefficients are described as samples of a fixed Gaussian process that are rotated and scaled according to a set of hidden variables representing the local image contrast and orientation. An optimal Bayesian least squares estimator is developed by conditioning upon and integrating over the hidden orientation and scale variables. The resulting denoising procedure gives results that are visually superior to those obtained with a Gaussian scale mixture model that does not explicitly incorporate local image orientation.

Original languageEnglish (US)
Title of host publication2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings
Pages1433-1436
Number of pages4
DOIs
StatePublished - 2006
Event2006 IEEE International Conference on Image Processing, ICIP 2006 - Atlanta, GA, United States
Duration: Oct 8 2006Oct 11 2006

Publication series

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

Other

Other2006 IEEE International Conference on Image Processing, ICIP 2006
CountryUnited States
CityAtlanta, GA
Period10/8/0610/11/06

Keywords

  • Image processing
  • Image restoration

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Fingerprint Dive into the research topics of 'Image denoising with an orientation-adaptive Gaussian scale mixture model'. Together they form a unique fingerprint.

Cite this