  Robust methods have been a successful approach to deal with contaminations
and noises in image processing. In this paper, we introduce a new robust method
for two-dimensional autoregressive models. Our method, called BMM-2D, relies on
representing a two-dimensional autoregressive process with an auxiliary model
to attenuate the effect of contamination (outliers). We compare the performance
of our method with existing robust estimators and the least squares estimator
via a comprehensive Monte Carlo simulation study which considers different
levels of replacement contamination and window sizes. The results show that the
new estimator is superior to the other estimators, both in accuracy and
precision. An application to image filtering highlights the findings and
illustrates how the estimator works in practical applications.
