Training and Using the Deep Learning Wavefront Sensor

Camilo Weinberger; Felipe Guzman; Esteban Vera

doi:10.1364/3D.2020.JW2A.24

Imaging and Applied Optics Congress
OSA Technical Digest (Optica Publishing Group, 2020),
paper JW2A.24
•https://doi.org/10.1364/3D.2020.JW2A.24

Training and Using the Deep Learning Wavefront Sensor

Camilo Weinberger, Felipe Guzman, and Esteban Vera

3D Image Acquisition and Display: Technology, Perception and Applications 2020

Washington, DC United States
22–26 June 2020
ISBN: 978-1-943580-77-4

From the session
Joint Poster Session II (JW2A)

Not Accessible

Your library or personal account may give you access

Get PDF
Email
Share
Get Citation
Copy Citation Text
C. Weinberger, F. Guzman, and E. Vera, "Training and Using the Deep Learning Wavefront Sensor," in Imaging and Applied Optics Congress, OSA Technical Digest (Optica Publishing Group, 2020), paper JW2A.24.

Export Citation
- BibTex
- Endnote (RIS)
- HTML
- Plain Text
Citation alert
Save article

Abstract

We propose an Adaptive Optics system controlled by a Slow Deep Learning Wavefront Sensor architecture that can predict and correct the first Zernike modes of low refresh frequency atmospheric effects to anticipate the AO in astronomical observations.

PDF Article

More Like This

Wavefront Reconstruction for Noisy Shack-Hartmann Wavefront Sensors using Deep Learning

Harshil Dave, Theodore B. DuBose, and Abbie T. Watnik
PF1C.6 Propagation Through and Characterization of Atmospheric and Oceanic Phenomena (pcAOP) 2021

Deep Learning for Turbulence-Tolerant Wavefront Reconstruction of Shack-Hartmann Wavefront Sensors

Theodore B. DuBose, Dennis F. Gardner, and Abbie T. Watnik
JW4G.6 Adaptive Optics: Analysis, Methods & Systems (AO) 2020

All-fibre focal-plane wavefront sensor

B.R.M. Norris, J. Wei, C.H. Betters, A. Wong, and S.G. Leon-Saval
PDP_1 Conference on Lasers and Electro-Optics/Pacific Rim (CLEO/PR) 2020

Previous Article Next Article

Poster Presentation

Media 1: PDF (436 KB)