Image enhancement by estimated a priori information

Two different a priori source probabilistic information functions are formulated with estimated probable strengths and variances of source elements. Correspondingly, two solutions for maximizing a posteriori probability with the different a priori source information are presented via Bayes' Law. Iterative imaging algorithms for the solutions are derived by employing the expectation-maximization technique of Demspter et al. These imaging algorithms are applied to computer generated and experimental phantom imaging data and improved images are obtained, compared to that of standard maximum likelihood algorithm.