Wavelet denoising of multicomponent images using gaussian scale mixture models and a noise-free image as priors

Paul Scheunders; Steve De Backer

doi:10.1109/tip.2007.899598

Wavelet denoising of multicomponent images using gaussian scale mixture models and a noise-free image as priors

IEEE Trans Image Process. 2007 Jul;16(7):1865-72. doi: 10.1109/tip.2007.899598.

Authors

Paul Scheunders¹, Steve De Backer

Affiliation

¹ Vision Lab, Department of Physics, University of Antwerp, 2610 Wilrijk, Belgium. paul.scheunders@ua.ac.be

PMID: 17605384
DOI: 10.1109/tip.2007.899598

Abstract

In this paper, a Bayesian wavelet-based denoising procedure for multicomponent images is proposed. A denoising procedure is constructed that (1) fully accounts for the multicomponent image covariances, (2) makes use of Gaussian scale mixtures as prior models that approximate the marginal distributions of the wavelet coefficients well, and (3) makes use of a noise-free image as extra prior information. It is shown that such prior information is available with specific multicomponent image data of, e.g., remote sensing and biomedical imaging. Experiments are conducted in these two domains, in both simulated and real noisy conditions.

MeSH terms

Algorithms*
Artifacts*
Artificial Intelligence*
Computer Simulation
Image Enhancement / methods*
Image Interpretation, Computer-Assisted / methods*
Models, Statistical
Normal Distribution
Stochastic Processes