A rigid POCS extension to a Poisson super-resolution algorithm

A model-based iterative Poisson maximum aposteriori (MAP) algorithm is extended to include a rigid projection on convex sets (POCS) step. Such an extension yields a significantly faster convergence to a perceptually and measurably better estimate of a class of objects. Both the MAP and POCS approaches have been shown to be highly successful at incorporating prior knowledge into nonlinear restoration schemes. This work shows a method for improving the restoration capabilities of an iterative MAP deconvolution technique by using spatial constraints in a POCS enhancement which shrinks the space of feasible solutions and suggests directions for future refinements