A learning-based approach to event-based Shack-Hartmann wavefront sensing

Matthew R. Ziemann; Isabelle A. Rathbun; Christopher A. Metzler

doi:10.1117/12.3028285

7 October 2024 A learning-based approach to event-based Shack-Hartmann wavefront sensing

Matthew R. Ziemann, Isabelle A. Rathbun, Christopher A. Metzler

Proceedings Volume 13149, Unconventional Imaging, Sensing, and Adaptive Optics 2024; 1314915 (2024) https://doi.org/10.1117/12.3028285
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Abstract

We present a novel deep learning-based framework for event-based Shack-Hartmann wavefront sensing. This approach leverages a convolutional neural network (CNN) to directly reconstruct high-resolution wavefronts from event-based sensor data. Traditional wavefront sensors, such as the Shack-Hartmann sensor, face challenges such as measurement artifacts and limited bandwidth. By integrating event-based cameras—which offer high temporal resolution and data efficiency—with CNN-based reconstruction—which can learn strong spatiotemporal priors— our method addresses these limitations while simultaneously improving the quality of reconstruction. We evaluate our framework on simulated high-speed turbulence data, demonstrating a 73% improvement in reconstruction fidelity compared to existing methods. Additionally, our framework is capable of predictive wavefront sensing to reduce compensation latency and increase overall system bandwidth.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Matthew R. Ziemann, Isabelle A. Rathbun, and Christopher A. Metzler "A learning-based approach to event-based Shack-Hartmann wavefront sensing", Proc. SPIE 13149, Unconventional Imaging, Sensing, and Adaptive Optics 2024, 1314915 (7 October 2024); https://doi.org/10.1117/12.3028285

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