A fast recursive two-dimensional estimation algorithm

A two-dimensional Kalman filtering scheme based on the Roesser local state-space model is given. This algorithm does not provide the conditional mean with respect to previously scanned data; rather, it generates the best gain sequence with respect to local data and prediction. This procedure is also compared experimentally to an optimal filter on noisy images, and it is observed that similar performances in terms of minimum mean square error and an enormous reduction in computations are obtained