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

Abstract

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.