DocumentCode :
1713843
Title :
Analysis of multiresolution image denoising schemes using generalized-Gaussian priors
Author :
Moulin, Pierre ; Liu, Juan
Author_Institution :
Beckman Inst. for Adv. Sci. & Technol., Illinois Univ., Urbana, IL, USA
fYear :
1998
Firstpage :
633
Lastpage :
636
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
Keywords :
AWGN; Bayes methods; Gaussian processes; image resolution; noise; parameter estimation; wavelet transforms; AWGN; Bayesian estimation; generalized-Gaussian priors; heavy-tailed priors; image processing; log prior; multiresolution image denoising; shrinkage rules; universal thresholding schemes; wavelet shrinkage methods; Bayesian methods; Image analysis; Image denoising; Image resolution; Performance analysis; Signal analysis; Signal resolution; Statistical analysis; Wavelet coefficients; Wavelet domain;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Time-Frequency and Time-Scale Analysis, 1998. Proceedings of the IEEE-SP International Symposium on
Conference_Location :
Pittsburgh, PA
Print_ISBN :
0-7803-5073-1
Type :
conf
DOI :
10.1109/TFSA.1998.721504
Filename :
721504
Link To Document :
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