High-fidelity, model-driven deep learning network for phase-only computer-generated holography (Conference Presentation)

Liangcai Cao; Kexuan Liu; Jiachen Wu; Zehao He

doi:10.1117/12.2655512

14 March 2023 High-fidelity, model-driven deep learning network for phase-only computer-generated holography (Conference Presentation)

Liangcai Cao, Kexuan Liu, Jiachen Wu, Zehao He

Author Affiliations +

Proceedings Volume PC12443, Advances in Display Technologies XIII; PC1244309 (2023) https://doi.org/10.1117/12.2655512
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

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 (14 March 2023); https://doi.org/10.1117/12.2655512

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KEYWORDS

Computer generated holography

Computer networks

Holograms

Convolution

Machine learning

Near field diffraction

Neural networks

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