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.
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