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 language | English (US) |
|---|---|
| Pages | 633-636 |
| Number of pages | 4 |
| State | Published - Jan 1 1998 |
| Event | Proceedings of the 1998 IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis - Pittsburgh, PA, USA Duration: Oct 6 1998 → Oct 9 1998 |
Other
| Other | Proceedings of the 1998 IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis |
|---|---|
| City | Pittsburgh, PA, USA |
| Period | 10/6/98 → 10/9/98 |
Fingerprint
ASJC Scopus subject areas
- Engineering(all)
Cite this
Analysis of multiresolution image denoising schemes using generalized-Gaussian priors. / Moulin, Pierre; Liu, Juan.
1998. 633-636 Paper presented at Proceedings of the 1998 IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis, Pittsburgh, PA, USA, .Research output: Contribution to conference › Paper
}
TY - CONF
T1 - Analysis of multiresolution image denoising schemes using generalized-Gaussian priors
AU - Moulin, Pierre
AU - Liu, Juan
PY - 1998/1/1
Y1 - 1998/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0031618103&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0031618103&partnerID=8YFLogxK
M3 - Paper
AN - SCOPUS:0031618103
SP - 633
EP - 636
ER -