Experimental implementation of wavefront sensorless real-time adaptive optics aberration correction control loop with a neural network

Minzhao Liu; David N. Lopez; Gabriel C. Spalding

doi:10.1117/12.2569647

20 August 2020 Experimental implementation of wavefront sensorless real-time adaptive optics aberration correction control loop with a neural network

Minzhao Liu, David N. Lopez, Gabriel C. Spalding

Author Affiliations +

Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 114691S (2020) https://doi.org/10.1117/12.2569647
Event: SPIE Nanoscience + Engineering, 2020, Online Only

Conference Poster

Abstract

Recently, deep neural network (DNN) based adaptive optics systems were proposed to address the issue of latency in existing wavefront sensorless (WFS-less) aberration correction techniques. Intensity images alone are sufficient for the DNN model to compute the necessary wavefront correction, removing the need for iterative processes and allowing practical real-time aberration correction to be implemented. Specifically, we generate the desired aberration correction phase profiles utilizing a DNN based system that outputs a set of coefficients for 27 terms of Zernike polynomials. We present an experimental realization of this technique using a spatial light modulator (SLM) on real physical turbulence-induced aberration. We report an aberration correction rate of 20 frames per second in this laboratory setting, accelerated by parallelization on a graphics processing unit. There are a number of issues associated with the practical implementation of such techniques, which we highlight and address in this paper.

Conference Presentation

Citation Download Citation

Minzhao Liu, David N. Lopez, and Gabriel C. Spalding "Experimental implementation of wavefront sensorless real-time adaptive optics aberration correction control loop with a neural network", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114691S (20 August 2020); https://doi.org/10.1117/12.2569647

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