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Proceedings Paper

Robust sensorless wavefront sensing via neural network in a single-shot
Author(s): Yuanlong Zhang; Hao Xie; Qionghai Dai
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Paper Abstract

Sensorless adaptive optics (AO) has been widely used in optical microscopy to improve imaging quality in scattering tissue without additional wavefront sensing devices. The traditional image metric-based sensorless AO method requires multiple frames to assess aberrated wavefront, which is time consuming and even inaccurate when the aberration becomes large due to distortion mode crosstalk. Here we propose a neural network based wavefront sensing method which can accurately predict wavefront distortions across different aberration scales in a single-shot. Compared to the traditional method, the neural network approach reduces the prediction time by over one thousand folds. We validate the superior performances of neural network-based approach in both accuracy and speed through numerical simulations.

Paper Details

Date Published: 17 February 2020
PDF: 7 pages
Proc. SPIE 11248, Adaptive Optics and Wavefront Control for Biological Systems VI, 112480E (17 February 2020); doi: 10.1117/12.2545158
Show Author Affiliations
Yuanlong Zhang, Tsinghua Univ. (China)
Hao Xie, Tsinghua Univ. (China)
Qionghai Dai, Tsinghua Univ. (China)

Published in SPIE Proceedings Vol. 11248:
Adaptive Optics and Wavefront Control for Biological Systems VI
Thomas G. Bifano; Sylvain Gigan; Na Ji, Editor(s)

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