Recognizing Optical Vortex Modes in Ultralow Illuminating Power With Convolutional Neural Network

Abstract

Vortex beams' orbital angular momentum (OAM) has important application value in optical communication and other fields. Accurate measurement of their OAM topological charges is required for the application of vortex beams in the field of optical communication. As a type of convolutional neural network, DenseNet which combines the “Data Augmentation with Expansion” method, was introduced to measure the fractional topological charge of OAM images in the case of few samples. The OAM images used in this paper generally do not have high brightness, which simulates ultra-long-distance transmission situations in reality. The experiment shows that the classification accuracy of DenseNet network reaches 98.77%, with an average training time of about 1100 seconds, which is short. Our results showcase the potential of convolutional neural network approach to further study the OAM light in the application of free-space optical communication.

Keywords

• Orbital angular momentum (OAM)
• topological charge
• free space optical communication
• convolutional neural network (CNN)
• pattern recognition