A fast multichannel approach to adaptive estimation and filtering of two dimensional images

A fast, computationally efficient method for adaptive image estimation is presented in this paper. The method is based on the multichannel form of the Fast-A-Posteriori-Error Sequential Technique (FAEST) for the estimation of the parameters of 2-D autoregressive (AR) models. The above models are appropriately designed to have the shift invariance property necessary for fast recursive least squares techniques. Moreover inclusion of a forgetting factor, or of a sliding window in the algorithm, permits adaptive image model parameter estimation. The specific properties of the algorithm are examined and it is shown that the proposed estimation scheme is much faster than other existing approaches. Various interesting applications of the method are discussed and examples are given which illustrate the above theoretical results.