Deep Learning-Based Real-Time Mode Decomposition for Multimode Fibers
Yi An, Liangjin Huang, Jun Li, Jinyong Leng, Lijia Yang, Pu Zhou
Abstract
Mode decomposition (MD) is essential to reveal the intrinsic mode properties of multimode fibers (MMFs). Real-time MD provides a powerful tool to analyze the dynamics in MMFs. In this paper, we demonstrated that real-time MD can be achieved with the help of deep learning technique. We use large amounts of simulated beam intensity profiles of MMFs to train a convolutional neural network (CNN) and then evaluated this trained CNN on both simulation and experimental data. When testing on the simulated beam profiles, the averaged correlation between the reconstructed patterns and measured patterns is above 0.9842 and the decomposing rate can reach about 200 Hz. While for the experimental case, the averaged correlation is above 0.8896 and the decomposing rate for modal weights is 29.9 Hz, which is restricted by the maximum frame rate of the CCD camera. The results of both simulation and experiment show the superb real-time ability of the deep learning-based MD methods.