Deep learning wavefront sensing method for Shack-Hartmann sensors with sparse sub-apertures
Deep learning wavefront sensing method for Shack-Hartmann sensors with sparse sub-apertures
复制标题
稀疏子孔径Shack-Hartmann传感器的深度学习波前传感方法
DOI：
10.1364/oe.427261
复制
发表时间：
2021-05-24
期刊：
影响因子：
3.8
通讯作者：
Jiang, Zongfu
中科院分区：
文献类型：
--
作者：
He, Yulong;Liu, Zhiwei;Jiang, Zongfu
在这封信中，我们提出了一种用于Shack-Hartmann传感器（SHWFS）的深度学习波前传感方法，可以在不直接计算质心的情况下从子孔径图像中预测波前。该方法可以用较少的子孔径精确重建高空间频率波前，突破了SHWFS探测大气湍流时d/r（0）近似等于1（d为子孔径直径，r（0）为大气相干长度）的限制。此外，我们使用迁移学习来加速训练过程，与基于深度学习的方法相比，训练时间减少了98.4%。数值模拟验证了我们的方法，平均剩余波前均方根（RMS）为0.08 λ。该方法为SHWFS探测大气湍流提供了一个新的方向。(C)根据OSA开放获取出版协议的条款，2021年美国光学学会
In this letter, we proposed a deep learning wavefront sensing approach for the Shack-Hartmann sensors (SHWFS) to predict the wavefront from sub-aperture images without centroid calculation directly. This method can accurately reconstruct high spatial frequency wavefronts with fewer sub-apertures, breaking the limitation of d/r(0) approximate to 1 (d is the diameter of sub-apertures and r(0) is the atmospheric coherent length) when using SHWFS to detect atmospheric turbulence. Also, we used transfer learning to accelerate the training process, reducing training time by 98.4% compared to deep learning-based methods. Numerical simulations were employed to validate our approach, and the mean residual wavefront root-mean-square (RMS) is 0.08 lambda. The proposed method provides a new direction to detect atmospheric turbulence using SHWFS. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement