Two-dimensional recursive parameter identification for adaptive Kalman filtering

The authors study the development of a two-dimensional (2-D) adaptive Kalman filtering by recursive adjustment of the parameters of an autoregressive (AR) image model with a nonsymmetric half-plane (NSHP) region of support. The image and degradation models are formulated in a 2-D state-space model, for which the relevant 2-D Kalman filtering equations are given. The recursive parameter identification is achieved using the extension of the stochastic Newton approach to the 2-D case. This process can be implemented online to estimate the image model parameters based upon the local statistics in every processing window. Simulation results for removing an additive noise from a degraded image are presented