Complex amplitude field reconstruction in atmospheric turbulence based on deep learning

Abstract

In this paper, we use deep neural networks (DNNs) to simultaneously reconstruct the amplitude and phase information of the complex light field transmitted in atmospheric turbulence based on deep learning. The results of amplitude and phase reconstruction by four different training methods are compared comprehensively. The obtained results indicate that the training method that can more accurately reconstruct the complex amplitude field is to input the amplitude and phase pattern pairs into the neural network as two channels to train the model.