DuBose Theodore B, Gardner Dennis F, Watnik Abbie T
Opt Lett. 2020 Apr 1;45(7):1699-1702. doi: 10.1364/OL.389895.
The Shack-Hartmann wavefront sensor (SH-WFS) is known to produce incorrect measurements of the wavefront gradient in the presence of non-uniform illumination. Moreover, the most common least-squares phase reconstructors cannot accurately reconstruct the wavefront in the presence of branch points. We therefore developed the intensity/slopes network (ISNet), a deep convolutional-neural-network-based reconstructor that uses both the wavefront gradient information and the intensity of the SH-WFS's subapertures to provide better wavefront reconstruction. We trained the network on simulated data with multiple levels of turbulence and compared the performance of our reconstructor to several other reconstruction techniques. ISNet produced the lowest wavefront error of the reconstructors we evaluated and operated at a speed suitable for real-time applications, enabling the use of the SH-WFS in stronger turbulence than was previously possible.
众所周知，在存在非均匀照明的情况下，夏克-哈特曼波前传感器（SH-WFS）会产生波前梯度的错误测量值。此外，在存在分支点的情况下，最常见的最小二乘相位重建器无法准确重建波前。因此，我们开发了强度/斜率网络（ISNet），这是一种基于深度卷积神经网络的重建器，它利用波前梯度信息和SH-WFS子孔径的强度来提供更好的波前重建。我们在具有多个湍流水平的模拟数据上训练了该网络，并将我们的重建器的性能与其他几种重建技术进行了比较。ISNet在我们评估的重建器中产生了最低的波前误差，并且以适合实时应用的速度运行，使得SH-WFS能够在比以前更强的湍流中使用。
