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 |
ASJC Scopus subject areas
- Engineering(all)
Fingerprint Dive into the research topics of 'Analysis of multiresolution image denoising schemes using generalized-Gaussian priors'. Together they form a unique fingerprint.
Cite this
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, .