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Robust methods have been a successful approach to deal with contaminationsand noises in image processing. In this paper, we introduce a new robust methodfor two-dimensional autoregressive models. Our method, called BMM-2D, relies onrepresenting a two-dimensional autoregressive process with an auxiliary modelto attenuate the effect of contamination (outliers). We compare the performanceof our method with existing robust estimators and the least squares estimatorvia a comprehensive Monte Carlo simulation study which considers differentlevels of replacement contamination and window sizes. The results show that thenew estimator is superior to the other estimators, both in accuracy andprecision. An application to image filtering highlights the findings andillustrates how the estimator works in practical applications.

Robust Estimation for Two-Dimensional Autoregressive Processes Based on Bounded Innovation Propagation Representations