Bayesian wavelet-based image estimation using noninformative priors

The sparseness and decorrelation properties of the discrete wavelet transform have been exploited to develop powerful denoising methods. Most schemes use arbitrary thresholding nonlinearities with ad hoc parameters, or employ computationally expensive adaptive procedures. We overcome these deficiencies with a new wavelet-based denoising technique derived from a simple empirical Bayes approach based on Jeffreys' non-informative priors. Our approach is a step towards objective Bayesian wavelet-based denoising. The result is a remarkably simple fixed non-linear shrinkage/thresholding rule which performs better than other more computationally demanding methods.