IDENTIFICATION OF IMAGE AND BLUE PARAMETERS IN FREQUENCY-DOMAIN USINGTHE EM ALGORITHM

In this correspondence, we extend the method presented in a recent pap er, which considers the problem of the semicausal autoregressive (AR) parameter identification for images degraded by observation noise only . We propose a new approach to identify both the causal and semicausal AR parameters and blur parameters without a priori knowledge of the o bservation noise power and the PSF of the degradation. We decompose th e image into I-D independent complex scalar subsystems resulting from the vector state-spare model by using the unitary discrete Fourier tra nsform (DFT). Then, by applying the expectation-maximization (Ehl) alg orithm to each subsystem, we identify the AR model and blur parameters of the transformed image, The AR parameters of the original image are then identified by using the least squares (LS) method, The restored image is obtained as a byproduct of the Ehl algorithm.