Random cascades of Gaussian scale mixtures and their use in modeling natural images with application to denoising

M. J. Wainwright, E. P. Simoncelli, A. S. Willsky

Research output: Contribution to conferencePaper

Abstract

A semi-parametric class of non-Gaussian multiscale statistical processes defined by random cascades on wavelet trees were developed. It was shown that the models accurately fit both the marginal and joint histograms of wavelet coefficients from natural images. In addition, applications of such models to denoising both 1D signals and natural images were highlighted.

Original languageEnglish (US)
Pages260-263
Number of pages4
StatePublished - Dec 1 2000
EventInternational Conference on Image Processing (ICIP 2000) - Vancouver, BC, Canada
Duration: Sep 10 2000Sep 13 2000

Other

OtherInternational Conference on Image Processing (ICIP 2000)
CountryCanada
CityVancouver, BC
Period9/10/009/13/00

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Cite this

Wainwright, M. J., Simoncelli, E. P., & Willsky, A. S. (2000). Random cascades of Gaussian scale mixtures and their use in modeling natural images with application to denoising. 260-263. Paper presented at International Conference on Image Processing (ICIP 2000), Vancouver, BC, Canada.