A Bayesian derivation of an iterative autofocus/super-resolution algorithm

Abstract

The author derives an estimate-maximize formulation of a Bayesian super-resolution algorithm for reconstructing scattering cross sections from coherent images. He generalizes this result to obtain an 'autofocus/super-resolution' method, which simultaneously autofocuses an imaging system and super-resolves its image data. He presents an explanatory numerical example to illustrate the implementation of the method on images of single- and double-point targets that are defocused by O (depth of focus). These are successfully super-resolved by autofocus/super-resolution, but not by pure super-resolution. He conjectures that autofocus/super-resolution might usefully by applied to the interpretation of airborne synthetic aperture radar images that are subject to defocusing effects.