Identification and restoration of noisy blurred images using the expectation-maximization algorithm

Abstract

-In image restoration, it is nearly always assumed that the point-spread function of the degrading system, as well as the variance of the observation noise and a model of the original image, are known a priori. Since these parameters are unknown for practical images of interest, they have to be estimated from the noisy blurred images them- selves. This paper presents a maximum likelihood approach to the blur identification problem, and proposes to employ the expectation-max- imization algorithm to optimize the nonlinear likelihood function in an efficient way. In order to improve the performance of the identification algorithm, low-order parametric image and blur models are incorpo- rated into the identification method. The resulting iterative technique simultaneously identifies and restores noisy blurred images.