Wavefront distortion correction of superposed optical vortices based on deep multi-branch compensation network

Abstract

Optical vortices with orbital angular momentum play a crucial role in many fields such as target detection, quantum entanglement, and space optical communication. However, the wavefront distortion of optical vortices caused by atmospheric turbulence during free-space transmission is a major obstacle to practical applications. In this work, a deep-learning-based method is proposed for wavefront correction of distorted superposed optical vortices under various turbulence intensities. First, the Zernike polynomial expansion method is used to numerically simulate the atmospheric turbulence phase screen conforming to the Kolmogorov statistical law which assumes that turbulence is locally uniform and isotropic, in which the first 10 Zernike polynomials are adopted. Then, according to the distinct wavefront distortion features of different polynomials, a deep multi-branch compensation network (DMCN) is designed to adaptively and more accurately learn the mapping relationship between the intensity distribution profile of distorted optical vortices and the corresponding turbulent phase screen. The distorted superposed optical vortices dataset for network training is obtained by experimental measurement. Finally, the turbulence compensation screen predicted by the trained DMCN is verified experimentally and the results show that our wavefront distortion correction method based on DMCN can accurately predict the turbulent compensation screen and improve the mode purity of superposed optical vortices to more than 80% within 40 ms when using a workstation computer under various turbulence intensities, which has apparent advantages compared with traditional algorithms.