Statistical models of a priori information for image processing

A probabilistic formulation of several statistical models of the a priori source information is presented with maximum entropy analysis on individual source element behavior and Bernoulli counting process on source strength correlation. The maximum likelihood and maximum entropy image restoring of Frieden is shown to be a special case of these models when each source element has a uniform probabilistic distribution with all of them having the same constrained distribution range in treating data as a likelihood constraint. A maximum a posteriori probability analysis of incorporating both the a priori source and data information into account is extensively studied. Iterative imaging algorithms are derived by employing the expectation-maximization technique of Dempster et al. These algorithms are applied to computer generated and experimental radioisotope phantom imaging data. Improved images are obtained, compared to that of standard maximum likelihood algorithms of Shepp et al and Lange et al.