Fast recursive estimation of the parameters of a space-varying autoregressive image model

The identification of two-dimensional (2-D) autoregressive (AR) image models has been previously shown to be an integral part of image estimation. Furthermore, because of the nonhomogeneous nature of images, much better results are obtained with space varying models. To this effect, the development of a fast recursive method is now proposed for estimating the parameters of a two-dimensional AR image model, at each pixel, based on a finite memory. This fast method can be coupled to a space-variant Kalman filter for on-line adaptive estimation or can be used to estimate 2-D spectra for space-variant fields.