A Bayesian approach to 2D non minimum phase AR identification
G. Jacovitti, Alessandro Neri · 1990
The authors deal with estimation of autoregressive (AR) noncausal models of bidimensional signals. The problem of factorizing an image into an excitation with a given marginal p.d.f. and a IIR filter is formulated in a Bayesian conceptual framework. The proposed solution is an iterative procedure for the minimization of the a posteriori risk associated to a given cost function. The procedure implies the inversion of a Toeplitz-block-Toeplitz covariance matrix and the iterated solution of a set of normal equations associated with a nonlinear estimation stage.>