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

Harshil Dave; Theodore B. DuBose; Abbie T. Watnik

doi:10.1364/PCAOP.2021.PF1C.6

OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP)
OSA Technical Digest (Optica Publishing Group, 2021),
paper PF1C.6
•https://doi.org/10.1364/PCAOP.2021.PF1C.6

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

Harshil Dave, Theodore B. DuBose, and Abbie T. Watnik

Propagation Through and Characterization of Atmospheric and Oceanic Phenomena 2021

Washington, DC United States
19–23 July 2021
ISBN: 978-1-943580-89-7

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Adaptive Optics (PF1C)

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H. Dave, T. B. DuBose, and A. T. Watnik, "Wavefront Reconstruction for Noisy Shack-Hartmann Wavefront Sensors using Deep Learning," in OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP), H. Hua, B. Javidi, M. Martinez-Corral, O. Matoba, A. Stern, S. Thibault, T. Alieva, J. Ke, F. Willomitzer, F. Okten, P. Silveira, P. Banerjee, E. Stoykova, D. Chu, J. Park, F. Imai, C. Joo, M. Digman, D. Gardner, S. Gladysz, D. LeMaster, S. Basu, and O. Korotkova, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper PF1C.6.

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Abstract

A deep learning network for wavefront reconstruction based on intensity and slopes information (ISNet) is further improved by using realistic noise to generate a con- volutional neural net model for accurate centroiding and slopes calculation.

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