We examine methods for preprocessing a collection of atmospheric turbulence-degraded short-exposure imagery to improve the resolving power of estimation algorithms. We redefine the method known as frame selection in the context of optimizing estimation results. We compare several measures of image quality with idealized standards, demonstrating their relative ability to rank highly the least-degraded image frames. In particular, we find the Fisher information measure to be the most noise tolerant and robust frame-selection measure. We then examine the resolving implication of removing additive background noise resulting from the sky and telescope. Specifically, we show that background compensation acts as a de facto restoration of the compact object support and leads to furthering the resolving power of estimation methods. Results from simulated imaging scenarios demonstrate the improved ability of a multiframe maximum a posteriori estimator to restore the passband object distribution as well as to further recover the lost spectral content residing beyond the diffraction limit.

© 1999 Optical Society of America

[Optical Society of America ]

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