Wavelet denoising of multicomponent images using gaussian scale mixture models and a noise-free image as priors.
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.
Scheunders
Paul
P
Vision Lab, Department of Physics, University of Antwerp, 2610 Wilrijk, Belgium. paul.scheunders@ua.ac.be
De Backer
Steve
S
D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D016011:Normal Distribution; D013269:Stochastic Processes