Robust sensorless wavefront sensing via neural network in a single-shot

Yuanlong Zhang; Hao Xie; Qionghai Dai

doi:10.1117/12.2545158

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17 February 2020 Robust sensorless wavefront sensing via neural network in a single-shot

Yuanlong Zhang, Hao Xie, Qionghai Dai

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Proceedings Volume 11248, Adaptive Optics and Wavefront Control for Biological Systems VI; 112480E (2020) https://doi.org/10.1117/12.2545158
Event: SPIE BiOS, 2020, San Francisco, California, United States

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.

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Yuanlong Zhang, Hao Xie, and Qionghai Dai "Robust sensorless wavefront sensing via neural network in a single-shot", Proc. SPIE 11248, Adaptive Optics and Wavefront Control for Biological Systems VI, 112480E (17 February 2020); https://doi.org/10.1117/12.2545158

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