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20 August 2020 Experimental implementation of wavefront sensorless real-time adaptive optics aberration correction control loop with a neural network
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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.
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© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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