Phase Retrieval Using Estimation Methods For Intensity Correlation Imaging

Abstract

The angular resolution of an imaging system is sharply bounded by the diffraction limit, a fundamental property of electromagnetic radiation propagation. In order to increase resolution and see finer details of remote objects, the sizes of telescopes and cameras must be increased. As the size of the optics increase, practical problems and costs increase rapidly, making sparse aperture systems attractive for some cases. The method of Intensity Correlation Imaging (ICI) provides an alternative method of achieving high angular resolution that allows a system to be built with less stringent precision requirements, trading the mechanical complexity of a typical sparse aperture for increased computational requirements. Development of ICI has stagnated in the past due to the inadequacies of computational capabilities, but the continued development of computer technologies now allow us to approach the image reconstruction process in a new, more e ffctive manner. This thesis uses estimation methodology and the concept of transverse phase diversity to explore the modern bounds on the uses of ICI. Considering astronomical observations, the work moves beyond the traditional, single-parameter uses of ICI, and studies systems with many parameters and complex interactions. It is shown that ICI could allow significant new understanding of complex multi-star systems. Also considered are exoplanet and star-spot measurements; these are less promising due to noise considerations. Looking at the Earth imaging problem, we find significant challenges, particularly related to pointing requirements and the need for a large field-of-view. However, applying transverse phase diversity (TPD) measurements and a least-squares estimation methodology solves many of these problems and re-opens the possibility of applying ICI to the Earth-imaging problem. The thesis presents the TPD concept, demonstrates a sample design that takes advantage of the new development, and implements reconstruction techniques. While computational challenges remain, the concept is shown to be viable. Ultimately the work presented demonstrates that modern developments greatly enhance the potential of ICI. However, challenges remain, particularly those related to noise levels.