Noncausal modeling and restoration of noisy images

A noncausal ARMA model is employed to accurately account for the two-dimensional structure of imagery data. An equation error approach is taken to develop an observer-identifier for the identification of the image model parameters. The identified model is recast into a state equation form which serves as the basis for applying Kalman filtering equations. The resulting strip filter is applied to an image restoration problem. Simulated results are included to illustrate image model verification and the convergence of the model parameter observer-identifier. Also included are images demonstrating the effectiveness of the resulting image restoration filter.