Wavelet-based image estimation: An empirical Bayes approach using Jeffreys' noninformative prior

The sparseness and decorrelation properties of the discrete wavelet transfo rm have been exploited to develop powerful denoising methods. However, most of these methods have free parameters which have to be adjusted or estimat ed. In this paper, we propose a wavelet-based denoising technique without a ny free parameters; it is, in this sense, a "universal" method. Our approac h uses empirical Bayes estimation based on a Jeffreys' noninformative prior ; it is a step toward objective Bayesian wavelet-based denoising. The resul t is a remarkably simple fixed nonlinear shrinkage/thresholding rule which performs better than other more computationally demanding methods.