Abstract

The objective of this article is to present a new image restoration algorithm. First, each pixel in the image is classified into k categories. Then we assume that the gray levels in each category follow a nonsymmetric half-plane (NSHP) autoregressive model. Robust estimation of the parameters of the model is considered to attenuate the effect of the image contamination on the parameters. In each iteration we will construct a new image using a robustified version of the residuals. The introduction of the classification techniques as a first step of the algorithm reduces considerably the number of parameters to estimate. Hence, the computational time is also reduced because the robust estimations of the parameters are solutions of nonlinear system of equations. Some applications are presented to real synthetic aperture radar (SAR) images to illustrate how our algorithm restores an image in practice.