Maximum likelihood image identification and restoration based on the EM algorithm

Summary form only given. Simultaneous iterative identification and restoration have been treated. The image and the noise have been modeled as multivariate Gaussian processes. Maximum-likelihood estimation has been used to estimate the parameters that characterize the Gaussian processes, where the estimation of the conditional mean of the image represents the restored image. Likelihood functions of observed images are highly nonlinear with respect to these parameters. Therefore, it is in general very difficult to maximize them directly. The expectation-maximization (EM) algorithm has been used to find these parameters