Two-dimensional recursive estimation for ARMA signal models

Abstract

We present a recursive estimation algorithm for autoregressive moving average (ARMA) random field models. The estimator is an extension to ARMA models of the efficient reduced update Kalman filter (RUKF). We also discuss the identification of the ARMA model parameters from noise-free image data. The ARMA estimator is run on several sets of random field data as well as on real images. The experimental results are compared to those achievable using an AR model and RUKF on the same data fields. We find no significant improvement for comparable model orders.