3D-CGH-Net: Customizable 3D-hologram generation via deep learning

Computer generated holograms can create arbitrary light distributions through computation of light propagation. 3D-computer-generated-hologram generation requires significant computation time, especially so for 3D-computer-generated-holograms where it is important to calculate the interactions between different planes on top of the planes themselves. In this paper we propose a neural-network-based method for 3D-computer-generated-hologram generation in order to improve the hologram computation speed. The trained model can be used to generate holograms with arbitrary light propagation parameters. The neural network was numerically and optically tested against the GS algorithm using 3D-computer-generated-holograms with resolution up to 1024×1024 pixels. 3D-holograms with 16 object planes were generated, which is, to our knowledge, the highest number currently achieved with a neural-network-based-method. The experiments show that proposed model can create holograms significantly faster than some conventional algorithms and, overall, results better-quality images. The trained network can also be used using different propagation parameters, such as wavelength and focal distance.