Adaptive optics has been widely used in biological science to recover high-resolution optical image deep into the tissue, where optical distortion detection with high speed and accuracy is strongly required. Here, we introduce convolutional neural networks, one of the most popular machine learning models, into Shack–Hartmann wavefront sensor (SHWS) to simplify optical distortion detection processes. Without image segmentation or centroid positioning algorithm, the trained network could estimate up to 36th Zernike mode coefficients directly from a full SHWS image within 1.227[Formula: see text]ms on a personal computer, and achieves prediction accuracy up to 97.4%. The simulation results show that the average root mean squared error in phase residuals of our method is 75.64% lower than that with the modal-based SHWS method. With the high detection accuracy and simplified detection processes, this work has the potential to be applied in wavefront sensor-based adaptive optics for in vivo deep tissue imaging.

中文翻译：

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通过机器学习简化光学畸变的检测

自适应光学已广泛用于生物科学中，以恢复组织深处的高分辨率光学图像，其中强烈需要高速和准确的光学失真检测。在这里，我们将卷积神经网络（最流行的机器学习模型之一）引入 Shack-Hartmann 波前传感器 (SHWS) 以简化光学失真检测过程。在没有图像分割或质心定位算法的情况下，经过训练的网络可以在个人计算机上在 1.227 [公式：见文本] ms 内直接从完整的 SHWS 图像估计多达 36 个 Zernike 模式系数，并实现高达 97.4% 的预测精度。仿真结果表明，我们方法的相位残差的平均均方根误差比基于模态的 SHWS 方法低 75.64%。