(Georgia Institute of Technology, 2001-08)
Vidakovic, Brani; Ruggeri, Fabrizio
In this paper we address the problem of model-induced wavelet shrinkage. Assuming the independence model according to which the wavelet coefficients are treated individually, we discuss a level-adaptive Bayesian model in the wavelet domain that has two important properties: (i) it realistically describes empirical properties of signals and images in the wavelet domain, and (ii) it results in simple optimal shrinkage rules to be used in fast wavelet denoising. The proposed denoising paradigm BAMS (short for Bayesian Adaptive Multiresolution Shrinker) is illustrated on an array of Donoho and Johnstone's standard test functions and is compared to some standard wavelet-based smoothing methods.
(Georgia Institute of Technology, 1998-03)
Vidakovic, Brani
Wavelet shrinkage, the method proposed by the seminal work of Donoho and Johnstone is a disarmingly simple and efficient way of denoising data. Shrinking wavelet coefficients was proposed from several optimality criteria. In this article a wavelet shrinkage by coherent Bayesian inference in the wavelet domain is proposed. The methods are tested on standard Donoho-Johnstone test functions.