Method of inverting wavefront phase from far-field spot based on deep learning

In the adaptive optics system, the accuracy and robustness of wavefront sensor greatly affect the ability of aberration detection and closed-loop correction. In the condition of the nonuniformity of amplitude distributions or insufficient of beacon light energy, it will cause accuracy decrease of Hartmann wavefront sensing due to the lack of sub-aperture light. Meanwhile, the real-time performance of the wavefront sensing-free adaptive system based on far-field spot inversion cannot meet the practical requirements. The method of the wavefront inversion based on deep learning is to directly obtain aberrations by inputting the far-field light intensity image, which can be used as an effective supplement to the adaptive optical system. Through numerical simulation, this paper proved that the deep residual neural network could directly predict the Zernike coefficient of the wavefront phase through the far-field spot. And experimental demonstrated the corrected residual RMS between input and reconstructed wavefront phase was 0.08 waves, the average computation time was less than 2 ms by GPU acceleration. This method can predict the Zernike coefficient of incident wavefront distortion more accurately, and has a good aberration correction capability, suitable for measuring and correcting the main components of wavefront distortion in traditional adaptive optics method, or providing a good initial wavefront estimation for optimized adaptive optics.