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

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dc.contributor.authorWeddell, S.J.
dc.contributor.authorWebb, R.Y.
dc.date.accessioned2009-06-15T02:02:09Z
dc.date.available2009-06-15T02:02:09Z
dc.date.issued2008en
dc.description.abstractA 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.en
dc.identifier.citationWeddell, 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.en
dc.identifier.doihttps://doi.org/10.1109/JSTSP.2008.2004218
dc.identifier.issn1932-4553
dc.identifier.urihttp://hdl.handle.net/10092/2555
dc.language.isoen
dc.publisherUniversity of Canterbury. Electrical and Computer Engineeringen
dc.rights.urihttps://hdl.handle.net/10092/17651en
dc.subjectSignal Processingen
dc.subjectAdaptive Opticsen
dc.subjectNeural Networksen
dc.subjectFields of Research::240000 Physical Sciences::240100 Astronomical Sciences::240199 Astronomical sciences not elsewhere classifieden
dc.subject.marsdenFields of Research::240000 Physical Sciences::240400 Optical Physics::240401 Optics and opto-electronic physicsen
dc.titleReservoir Computing for Prediction of the Spatially-Variant Point Spread Functionen
dc.typeJournal Article
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