Shack-Hartmann wavefront reconstruction by deep learning neural network for adaptive optics

Zareb A. Noel; Timothy J. Bukowski; Stanislav Gordeyev; R. Mark Rennie

doi:10.1117/12.2677670

3 October 2023 Shack-Hartmann wavefront reconstruction by deep learning neural network for adaptive optics

Zareb A. Noel, Timothy J. Bukowski, Stanislav Gordeyev, R. Mark Rennie

Proceedings Volume 12693, Unconventional Imaging, Sensing, and Adaptive Optics 2023; 126930G (2023) https://doi.org/10.1117/12.2677670
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

Standard methods of Shack-Hartmann wavefront reconstruction rely on solving a system of linear equations, extracting wavefront estimates from measured wavefront slopes, which are calculated by retrieving centroids from a Shack-Hartmann Wavefront Sensor (SHWFS). As the dimensions of a micro-lens array in the SHWFS increase, the computational cost of processing wavefronts can become increasingly expensive. For applications that require rapid and accurate computations, such as closed-loop adaptive-optic systems, traditional centroiding and the least-squares reconstruction becomes the main bottleneck limiting performance. In this work, we apply a convolutional neural network (CNN) approach to directly reconstruct wavefronts from raw SHWFS measurements, circumventing both bottlenecks. The CNN model utilizes the ResU-Net framework to perform a zonal wavefront reconstruction, and a method for preprocessing the raw data was investigated with the prospect of enhancing the accuracy of this model specifically for the zonal approach to wavefront reconstruction.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zareb A. Noel, Timothy J. Bukowski, Stanislav Gordeyev, and R. Mark Rennie "Shack-Hartmann wavefront reconstruction by deep learning neural network for adaptive optics", Proc. SPIE 12693, Unconventional Imaging, Sensing, and Adaptive Optics 2023, 126930G (3 October 2023); https://doi.org/10.1117/12.2677670

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