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Reservoir Computing for Prediction of the Spatially-Variant Point Spread Function

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2 Author(s)
Stephen J. Weddell ; Dept. of Electr. & Comput. Eng., Univ. of Canterbury, Christchurch ; Russell Y. Webb

A new method is presented which provides prediction of the spatially variant point spread function for the restoration of astronomical images, distorted by atmospheric turbulence when viewed using ground-based telescopes. Our approach uses reservoir computing to firstly learn the spatio-temporal evolution of aberrations caused by turbulence, and secondly, predicts the space-varying point spread function (PSF) for application of widely-used deconvolution algorithms, resulting in the restoration of astronomical images. In this article, a reservoir-based, recurrent neural network is used to predict modal aberrations that comprise the spatially variant PSF over a wide field-of-view using a time-series ensemble from multiple reference beacons.

Published in:

IEEE Journal of Selected Topics in Signal Processing  (Volume:2 ,  Issue: 5 )