Nonlinear image processing based on optimization of generalized information methods

Abstract

A range of nonlinear image reconstruction procedures based on extremizing the generalized Shannon entropy, Kullback-Leibler cross-entropy and Renyi information measures and proposed by the author in early papers is presented. The ``generalization'' assumes search for the solution over the space of real bipolar or complex functions. Such an approach allows, first, to reconstruct signals of any type and physical nature and, secondly, to decrease nonlinear intensity image distortions caused by measurement errors. All the elaborated procedures are contained in VLBI ``IMAGE'' program package developed in IAA RAS.