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Proceedings Paper

Bayes estimation of dynamic and fixed aberrations and object from phase-diverse speckle data
Author(s): Brian J. Thelen; Richard G. Paxman; John H. Seldin; David R. Rice
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Paper Abstract

In phase diverse speckle imaging, one collects a time series of phase-diversity image sets. From these data it is possible to jointly estimate the object and each realization of the aberrations. Current approaches model the total aberration phase screen in some deterministic, parametric fashion. For a typical scenario, however, one has more information than this. Specifically, the total aberration phase screen is caused by fixed aberrations combined with dynamic (time-varying), turbulence-induced aberrations for which we have some knowledge about the stochastic behavior. One important example is where the dynamic aberrations derive from Kolmogorov turbulence. In this context, utilizing this extra information has the potential for being a powerful aid in the joint aberration/object estimation. In addition, such a framework would provide a relatively simple method for calibrating fixed aberrations in an imaging system. The natural framework for utilizing the stochastic nature of the wavefronts is that of Bayesian statistical inference, where one imposes an a priori probability distribution on the turbulence-induced wavefronts. In this paper, we present the general Bayesian approach for this joint-estimation problem of the fixed aberrations, the dynamic aberrations, and the object from phase-diverse speckle data. We then discuss issues related to theoretical performance, numerical implementation, and applications. Finally we provide simulation results which demonstrate improvement in PDS image reconstructions resulting from the Bayesian estimation approach.

Paper Details

Date Published: 25 October 1996
PDF: 12 pages
Proc. SPIE 2827, Digital Image Recovery and Synthesis III, (25 October 1996); doi: 10.1117/12.255086
Show Author Affiliations
Brian J. Thelen, Environmental Research Institute of Michigan (United States)
Richard G. Paxman, Environmental Research Institute of Michigan (United States)
John H. Seldin, Environmental Research Institute of Michigan (United States)
David R. Rice, Environmental Research Institute of Michigan (United States)


Published in SPIE Proceedings Vol. 2827:
Digital Image Recovery and Synthesis III
Paul S. Idell; Timothy J. Schulz, Editor(s)

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