Modeling of random natural textures using a spatially variant multiplicative autoregressive model

A spatially variant seasonal multiplicative autoregressive model which treats an image as an inhomogeneous random process with a nonstationary mean and possibly nonstationary covariance is introduced. The principal features of this model are the ability to guarantee stability online, efficient one-dimensional recursive parameter estimation and explicit coefficient representation necessary for classification and synthesis applications