基于远场信息和卷积神经网络的波前重构方法

Wavefront reconstruction method based on far-field information and convolutional neural network

探测波前相位信息是实现自适应光学波前补偿的关键,使用卷积神经网络(CNN)代替波前传感器进行波前重构,系统简单易于实现,同时重构过程不依赖迭代运算,快速实时。为准确提取远场中的波前特征,CNN需要事先使用大量样本进行训练。研究中根据4~30阶大气湍流泽尼克像差系数与其远场强度的对应关系,仿真制作样本数据集,训练CNN从输入的一帧远场图像中预测出畸变波前的泽尼克像差系数,重构原始波前。验证结果表明,该方法能快速实时地还原出波前相位信息,重构波前较原始波前具有极高的波面吻合度和较小的残差剩余量,有望实现实际自适应光学系统中的闭环校正。

Detecting wavefront phase information is the key to realize adaptive optics wavefront compensation.Using convolutional neural network(CNN)instead of wavefront sensor for wavefront reconstruction,the system can be simple and easy to implement,and the reconstruction process is fast and real-time without iteration.To extract the wavefront features from the far field accurately,CNN needs to use a large number of samples for training in advance.In the study,according to the corresponding relationship between Zernike aberration coefficient of orders 4 to 30 and its far-field intensity,the sample data set was simulated,CNN was trained to predict the Zernike aberration coefficient of the distorted wavefront from an input far-field image,then reconstruct the original wavefront.The experimental results show that this method can restore the phase information of wavefront quickly and in real time.Compared with the original wavefront,the reconstructed wavefront has higher wavefront coincidence and smaller residual.It is expected to realize the closed-loop correction in practical adaptive optics systems.