Progress of phase-only holograms generation based on deep learning model-driven

Phase-only hologram has been widely noticed in the field of holographic display due to the characteristics of having no conjugate scattered light and almost no optical loss in the imaging process. Computer-generated holograms based on deep learning have great potential for generating high-efficiency and high-quality display holograms, and have an important position in the field of phase-only hologram generation. This paper introduces computational holography technology, traditional phase-only hologram generation algorithms and model-driven deep learning fundamentals, overviews deep learning model-driven computational holography solutions based on deep learning proposed in recent years, compares optimization methods based on the network structure and loss function, and looks forward to the development and challenges of deep learning technology in the field of computational holography.