Imaging with phase diversity: simulations with a neural network

Details Imaging with phase diversity: simulations with a neural network Imaging with phase diversity: simulations with a neural network

Apr 1993

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1993spie.1982..410m&link_type=abstract

Proc. SPIE Vol. 1982, p. 410-417, Photoelectronic Detection and Imaging: Technology and Applications '93, Li-wei Zhou; Ed.

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The technique of phase diversity was proposed for estimating telescope aberrations for an unknown extended object. The original version of phase diversity requires extensive processing due to a nonlinear optimization algorithm which is prohibitive in a real-time system. Therefore, neural networks were explored as an alternative solution of the problem and this paper shows the modification of the traditional phase diversity method to employ neural networks to estimate aberrations of point source and extended scene data. Simulations indicated aberrations could be estimated to an average error of 0.02 waves rms.

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