Regarding an imaging system as a communication channel on which information about an object is passed furnishes a powerful alternative to the usual picture in which the quality of an imaging system is measured in terms of the faithfulness with which it renders an object. Since the object is almost certainly not known in advance, the information theoretic picture based on only a statistical object model is likely to be superior. We assess the performance of astronomical imaging systems and iterative image recovery algorithms from the statistical information viewpoint, and show that phenomena like super-resolution characteristic of certain nonlinear iterative algorithms like Richardson-Lucy and MEM can be given a natural interpretation in terms of information theory. We also describe our preliminary work on the use of Fisher information to provide further insights into the nature of information recovery by imaging systems in the presence of photon counting and additive detection noise sources. Work is in progress to apply these ideas to real image data.