The combination of Computer-Generated Holography (CGH) and deep learning has opened the possibility to generate both real-time and high-quality holograms. However, the widely-used data-driven deep learning method faces the problem of the large number of labeled training datasets generated by traditional algorithms, such as Gerchberg–Saxton (GS) iterative algorithm. It always takes a long time and limits the training performance of the network. In this work, we propose a model-driven neural network for high-fidelity Phase-Only Hologram (POH) generation. The Fresnel diffraction process is introduced as the physical model, which makes the network can automatically learn the latent encodings of POHs in an unsupervised way. Furthermore, the sub-pixel convolution upsampling method effectively improves the reconstruction quality. Once the training is completed, the POH of any two-dimensional image can be quickly generated. The calculation time is one to two orders of magnitude faster.

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Liangcai Cao, Kexuan Liu, Jiachen Wu, and Zehao He, "High-fidelity, model-driven deep learning network for phase-only computer-generated holography (Conference Presentation)," Proc. SPIE PC12443, Advances in Display Technologies XIII, PC1244309 (Presented at SPIE OPTO: January 31, 2023; Published: 14 March 2023); https://doi.org/10.1117/12.2655512.6321350575112.