Fast and optimal multiframe blind deconvolution algorithm for high-resolution ground-based imaging of space objects.
We report a multiframe blind deconvolution algorithm that we have developed for imaging through the atmosphere. The algorithm has been parallelized to a significant degree for execution on high-performance computers, with an emphasis on distributed-memory systems so that it can be hosted on commodity clusters. As a result, image restorations can be obtained in seconds to minutes. We have compared and quantified the quality of its image restorations relative to the associated Cramér-Rao lower bounds (when they can be calculated). We describe the algorithm and its parallelization in detail, demonstrate the scalability of its parallelization across distributed-memory computer nodes, discuss the results of comparing sample variances of its output to the associated Cramér-Rao lower bounds, and present image restorations obtained by using data collected with ground-based telescopes.
Matson
Charles L
CL
Air Force Research Laboratory, 3550 Aberdeen Avenue SE, Kirtland Air Force Base, New Mexico 87117, USA. charles.matson@kirtland.af.mil
Borelli
Kathy
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Jefferies
Stuart
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Beckner
Charles C
CC
Hege
E Keith
EK
Lloyd-Hart
Michael
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