Analysis of multiresolution image denoising schemes using generalized-Gaussian priors

Pierre Moulin, Juan Liu

Research output: Contribution to conferencePaper

Abstract

In this paper, we investigate various connections between wavelet shrinkage methods in image processing and Bayesian estimation using Generalized Gaussian priors. We present fundamental properties of the shrinkage rules implied by Generalized Gaussian and other heavy-tailed priors. This allows us to show a simple relationship between differentiability of the log-prior at zero and the sparsity of the estimates, as well as an equivalence between universal thresholding schemes and Bayesian estimation using a certain Generalized Gaussian prior.

Original languageEnglish (US)
Pages633-636
Number of pages4
StatePublished - Jan 1 1998
EventProceedings of the 1998 IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis - Pittsburgh, PA, USA
Duration: Oct 6 1998Oct 9 1998

Other

OtherProceedings of the 1998 IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis
CityPittsburgh, PA, USA
Period10/6/9810/9/98

ASJC Scopus subject areas

  • Engineering(all)

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    Moulin, P., & Liu, J. (1998). Analysis of multiresolution image denoising schemes using generalized-Gaussian priors. 633-636. Paper presented at Proceedings of the 1998 IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis, Pittsburgh, PA, USA, .