Thumbnail
Access Restriction
Subscribed

Author Moulin, P. ♦ Juan Liu
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1998
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations ♦ Applied physics
Subject Keyword Image analysis ♦ Image resolution ♦ Image denoising ♦ Bayesian methods ♦ Wavelet coefficients ♦ Performance analysis ♦ Wavelet domain ♦ Signal resolution ♦ Statistical analysis ♦ Signal analysis
Abstract 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 the 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.
Description Author affiliation: Beckman Inst. for Adv. Sci. & Technol., Illinois Univ., Urbana, IL, USA (Moulin, P.)
ISBN 0780350731
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1998-10-09
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 499.21 kB
Page Count 4
Starting Page 633
Ending Page 636


Source: IEEE Xplore Digital Library