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

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

Nov 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008istsp...2..624w&link_type=abstract

IEEE Journal of Selected Topics in Signal Processing, Vol. 2, Issue 5, p.624-634

Astronomy and Astrophysics

Astronomy

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Reservoir Computing for Prediction of the Spatially-Variant Point Spread Function does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Reservoir Computing for Prediction of the Spatially-Variant Point Spread Function, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Reservoir Computing for Prediction of the Spatially-Variant Point Spread Function will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-747701