On the use of deep learning for computer-generated holography
Xuan Yu, Haomiao Zhang, Zhe Zhao, Xuhao Fan, Shaodong Hu, Zongjin Li, Wenbin Chen, Daqian Li, Shaoxi Shi, Wei Xiong, Hui Gao
Abstract
The research disciplines of computer-generated holography (CGH) and machine learning have evolved in parallel for decades and experienced booming growth due to breakthroughs in mathematical optimization and computing hardware. Over the past few years, deep learning has been applied to CGH and achieved remarkable success, accustoming a great step toward high-quality and real-time holographic display. This review introduces the fundamental concepts of CGH and deep learning, examines the development of deep-learning-based computer-generated holography (DLCGH), and explores cutting-edge research frontiers including data-driven models, physics-driven models, and jointly optimized models. Finally, we summarize with an outlook on the challenges and prospects of DLCGH.