Xu Yangjie, He Dong, Wang Qiang, Guo Hongyang, Li Qing, Xie Zongliang, Huang Yongmei
Institute of Optics and Electronics, Chinese Academy of Sciences, No.1 Guangdian Road, Chengdu 610209, China.
Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610209, China.
Sensors (Basel). 2019 Aug 23;19(17):3665. doi: 10.3390/s19173665.
In this paper, an improved method of measuring wavefront aberration based on image with machine learning is proposed. This method had better real-time performance and higher estimation accuracy in free space optical communication in cases of strong atmospheric turbulence. We demonstrated that the network we optimized could use the point spread functions (PSFs) at a defocused plane to calculate the corresponding Zernike coefficients accurately. The computation time of the network was about 6-7 ms and the root-mean-square (RMS) wavefront error (WFE) between reconstruction and input was, on average, within 0.1263 waves in the situation of D/r0 = 20 in simulation, where D was the telescope diameter and r0 was the atmospheric coherent length. Adequate simulations and experiments were carried out to indicate the effectiveness and accuracy of the proposed method.
本文提出了一种基于机器学习的改进的波前像差图像测量方法。该方法在强大气湍流情况下的自由空间光通信中具有更好的实时性能和更高的估计精度。我们证明，我们优化后的网络可以利用离焦平面上的点扩散函数（PSF）准确计算相应的泽尼克系数。在模拟中，当D/r0 = 20时（其中D为望远镜直径，r0为大气相干长度），网络的计算时间约为6 - 7毫秒，重建与输入之间的均方根（RMS）波前误差（WFE）平均在0.1263波长以内。进行了充分的模拟和实验以表明所提方法的有效性和准确性。
