Mat. Figueiredo et Rd. Nowak, Wavelet-based image estimation: An empirical Bayes approach using Jeffreys' noninformative prior, IEEE IM PR, 10(9), 2001, pp. 1322-1331
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.