The conventional Shack–Hartmann wavefront sensor (SHWS) requires wavefront slope measurements of every micro-lens for wavefront reconstruction. In this Letter, we applied deep learning on the SHWS to directly predict the wavefront distributions without wavefront slope measurements. The results show that our method could provide a lower root mean square wavefront error in high detection speed. The performance of the proposed method is also evaluated on challenging wavefronts, while the conventional approaches perform insufficiently. This Letter provides a new approach, to the best of our knowledge, to perform direct wavefront detection in SHWS-based applications.

中文翻译：

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深度学习辅助的Shack-Hartmann波前传感器可直接检测波前。

传统的Shack-Hartmann波前传感器（SHWS）需要对每个微透镜的波前斜率进行测量，以进行波前重建。在这封信中，我们在SHWS上应用了深度学习，无需进行波前斜率测量即可直接预测波前分布。结果表明，该方法可以在较高的检测速度下提供较低的均方根波前误差。所提出的方法的性能也在具有挑战性的波前上进行了评估，而传统方法的性能却不足。据我们所知，这封信提供了一种新方法，可以在基于SHWS的应用程序中执行直接波前检测。