The theory of two-dimensional spectral factorization is reviewed in the context of recursive modeling. The role of the Markov random field in recursive image modeling is then presented, Since spectral factorization in two-or higher-dimensions generally results in infinite order factors, it is necessary to perform Markov modeling after spectral factorization. The above concepts are then applied to the problem of Kalman filtering of images.