Progress of the Computer-Generated Holography Based on Deep Learning

Abstract

With the explosive developments of deep learning, learning–based computer–generated holography (CGH) has become an effective way to achieve real–time and high–quality holographic displays. Plentiful learning–based methods with various deep neural networks (DNNs) have been proposed. In this paper, we focus on the rapid progress of learning–based CGH in recent years. The generation principles and algorithms of CGH are introduced. The DNN structures frequently used in CGH are compared, including U–Net, ResNet, and GAN. We review the developments and discuss the outlook of the learning–based CGH.