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    This study introduces an event-based wavefront network (EBWFNet) for faster, more accurate Shack-Hartmann wavefront sensing (SHWFS). The novel CNN achieves sub-pixel accuracy in real-world conditions, outperforming existing event-based methods.

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    Area of Science:

    • Optics and Photonics
    • Machine Learning
    • Adaptive Optics

    Background:

    • Shack-Hartmann wavefront sensing (SHWFS) traditionally uses frame-based cameras for aberration measurement.
    • Conventional methods are limited by fixed sampling rates and inefficient pixel usage.
    • Event-based cameras offer asynchronous, high-speed data acquisition for SHWFS.

    Purpose of the Study:

    • To develop a novel convolutional neural network (CNN) for real-time, accurate spot centroid estimation in event-based SHWFS.
    • To evaluate the performance of the proposed EBWFNet in real-world scenarios.
    • To compare the EBWFNet against state-of-the-art event-based SHWFS techniques.

    Main Methods:

    • Development of a custom SHWFS hardware with synchronized frame- and event-based cameras.
    • Implementation of an event-based wavefront network (EBWFNet) using CNN architecture.
    • Unsupervised training and testing of the EBWFNet utilizing frame-based camera data.
    • Field testing and ablation studies to assess performance and component impact.

    Main Results:

    • The EBWFNet achieved highly accurate, sub-pixel spot centroid estimation in real-world conditions.
    • Demonstrated substantial improvement over existing state-of-the-art event-based SHWFS methods.
    • An unoptimized MATLAB implementation achieved speeds exceeding 800 Hz on a single GPU.

    Conclusions:

    • The proposed EBWFNet significantly enhances the accuracy and speed of event-based SHWFS.
    • Event-based cameras coupled with CNNs represent a promising advancement for adaptive optics.
    • The EBWFNet offers a robust and efficient solution for real-time wavefront aberration measurement.