Noise Identification and Estimation of its Statistical Parameters by Using Unsupervised Variational Classification
Benoît Vozel, Kacem Chehdi, Luc Klaine, Владимир Васильевич Лукин, Sergey Abramov · 2006
This paper deals with the problem of identifying the nature of the noise and estimating its statistical parameters from the observed image in order to be able to apply the most appropriate processing or analysis algorithm afterwards. We focus our attention on three main classes of degraded images, the first one being degraded by an additive noise, the second one by a multiplicative noise, and the latter by an impulse noise. To improve the identification rate, we propose an unsupervised variational classification through a multithresholding method. Each class is then characterized by statistical parameters obtained from homogeneous regions. For the accuracy of the estimation of the noise statistical parameters, we distinguish the corresponding local estimates statistical series according to the number of pixels taken into account to calculate them. The experimental study highlights the improvement so obtained and shows the efficiency and the robustness of the whole method