Robust image estimation in signal-dependent noise

Several estimators for the digital restoration of a noisy image are presented. These estimators differ from those previously found in the literature in that they are robust for deviations from the assumed signal or noise statistics, and the image is assumed to have been corrupted by signal-dependent or nonlinear noise, rather than simple additive noise. The assumption of signal-dependence complicates the problem considerably for non-robust estimators; for robust estimators, the problems are such that analytic solutions become impossible, and numerical methods must be used to derive the estimators. Both point-estimators and multi-parameter estimators are considered. In addition to the description of the various robust estimators, a comparison of their performance on real images corrupted by simulated signal-dependent noise with various (Gaussian and non-Gaussian) distributions is also presented.