Abstract

This paper is concerned with methods for preprocessing a collection of atmospheric turbulence-degraded short-exposure imagery to improver the resolving power of estimation algorithms. In the first portion of our paper we redefine the method known as frame selection in the context of optimization estimation results. Several measure of image quality are compared against idealized standards demonstrating their relative effectiveness to highly rank the least degraded image frames. In particular, we find the Fisher information measure to be the most noise tolerant and robust frame-selection measure. Results from simulated imaging scenarios demonstrate the improved ability of a multiframe maximum a-posteriori estimator to resolve the passband object distribution as well as to further recover the lost spectral content residing beyond the diffraction limit.

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