Robust image wavelet shrinkage for denoising

Donoho and Johnstone first introduced wavelet shrinkage as a denoising technique for signals embedded in Gaussian noise, but due to the linearity of wavelet decomposition, wavelet shrinkage is ineffective in non-Gaussian noise which exhibits outliers. In this paper, we evaluate two schemes which have been developed to extend the denoising capabilities of wavelet shrinkage to signals corrupted by non-Gaussian noise. The first scheme introduced by Bruce et al. smoother-cleaner wavelets integrates median filters into the wavelet decomposition. The second scheme, introduced by the authors, replaces the linear filters of wavelet decomposition with order statistic based Chameleon filters. We also show that a straight forward extension of these schemes to images does not offer the same effectiveness in denoising as they do with one dimensional signals.