Super-resolution of SAR images using Bayesian and convex set-theoretic approaches

The inherent poor resolution in several practical SAR image acquisition operations usually demands the processing of acquired imagery data for resolution enhancement before the data can be used for any target surveillance and classification purposes. Among the available restoration methods, super-resolution algorithms are primarily directed to re-creating some of the image spectral content (specifically the high frequency components that are responsible for the higher resolution) that is lost due to diffraction-limited sensing operations. While several different approaches for image super-resolution have been proposed in the recent past, iterative processing algorithms developed using statistical optimization methods have attained considerable prominence. In particular, a set of algorithms that attempt to maximize the likelihood of the image estimate by employing distribution functions that are simple to handle within an optimization framework have been shown to yield remarkable spectral extrapolation, and hence super-resolution, performance. Notwithstanding the powerful capabilities of these algorithms, a direct processing of SAR images with these could lead to generation of processing artifacts. Since the fundamental idea underlying the restoration and super-resolution processing is an intelligent utilization of a priori information (about the object or scene imaged), improved performance can be realized by incorporating constraint set modeling approaches in the maximization of the likelihood function. This paper will discuss the modeling of specific constraint sets and the design of processing algorithms using convex set models of the a priori information. The restoration and super-resolution performance of these algorithms will be described. We also discuss a few hybrid algorithms that combine the strong points of the Maximum Likelihood (ML) algorithm and the convex set based processing methods. Quantitative performance evaluation of these algorithms in processing SAR images is also given by application of these methods to MSTAR data.