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

Type of content
Journal Article
Thesis discipline
Degree name
Publisher
University of Canterbury. Electrical and Computer Engineering
Journal Title
Journal ISSN
Volume Title
Language
Date
2008
Authors
Weddell, S.J.
Webb, R.Y.
Abstract

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.

Description
Citation
Weddell, S.J., Webb, R.Y. (2008) Reservoir Computing for Prediction of the Spatially-Variant Point Spread Function. IEEE Journal of Selected Topics in Signal Processing, 2(5), pp. 624-634.
Keywords
Signal Processing, Adaptive Optics, Neural Networks, Fields of Research::240000 Physical Sciences::240100 Astronomical Sciences::240199 Astronomical sciences not elsewhere classified
Ngā upoko tukutuku/Māori subject headings
ANZSRC fields of research
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