Turbulence Profiling Neural Networks Using Imaging Shack-Hartmann Data for Wide-Field Image Correction

R. J. Hamilton, Michael Hart

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Wide-field image correction of turbulence-induced phase requires tomographic reconstruction of each layer of turbulence. Before reconstruction can occur, the layers must be counted and ranged. A new signal-to-noise ratio metric for detecting a single layer of turbulence in a multi-layer atmosphere from SLOpe Detection And Ranging (SLODAR) measurements of Shack-Hartmann wave-front sensor (SHWFS) data is presented. 12,000 1-4 layer atmosphere profiles are procedurally defined by Fried length, layer altitude, and a minimum layer SNR requirement. Each profile is measured in simulation by a SHWFS in a 1.5 meter telescope with a 2.5 arcminute field of view over a 200 millisecond window. The simulation outputs are used as a 5-fold cross validation training data set for convolutional neural networks (CNNs) that count and range layers. The counting network achieved 92.6% accuracy and all ranging networks scored above 97.8% validation accuracy. We find that layers with SNR below 1 accounted for a majority of the misclassified points for all networks. We conclude that CNNs are a good candidate for wide-field image correction systems imaging through turbulence due to their ability to accurately profile the atmosphere from short time windows of collected data.

Original languageEnglish (US)
Title of host publicationAdaptive Optics Systems VIII
EditorsLaura Schreiber, Dirk Schmidt, Elise Vernet
PublisherSPIE
ISBN (Electronic)9781510653511
DOIs
StatePublished - 2022
EventAdaptive Optics Systems VIII 2022 - Montreal, Canada
Duration: Jul 17 2022Jul 22 2022

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12185
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceAdaptive Optics Systems VIII 2022
Country/TerritoryCanada
CityMontreal
Period7/17/227/22/22

Keywords

  • SLODAR
  • adaptive optics
  • atmospheric tomography
  • neural networks
  • turbulence profiling
  • wavefront sensing
  • wide-field image correction

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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