Noise model discrimination for digital images based on variance-stabilizing transforms and on local statistics: Preliminary results

Most of the image restoration algorithms assumed the noise model and its parameters as an a priori information. Nevertheless this is not necessarily the case for real scenarios. Moreover, lack of knowledge about the noise parameters leads to heuristically approaches to choose the restoration algorithm´s parameters. Given a non-texture observed image, which can be noise-free or corrupted with some kind of noise (we consider Gaussian, Poisson, Gamma and Rayleigh) we propose a simple yet effective method to discriminate the noise model (or lack of) that corrupts the observed image by first applying a set of variance-stabilizing transforms and then proceed to estimate the variance using a local statistics estimator; the estimated variance will be unitary only for the particular variance-stabilizing transform that matches the correct noise model.