High-speed 3D hologram generation method via convolutional neural network

Jiaqing Dong; Minghao Liu; Zehao Sun; Zilong Li; Xuan Liu; Wenhua Zhong; Guijun Wang; Qiegen Liu; Xianlin Song

doi:10.1117/12.2688610

27 November 2023 High-speed 3D hologram generation method via convolutional neural network

Jiaqing Dong, Minghao Liu, Zehao Sun, Zilong Li, Xuan Liu, Wenhua Zhong, Guijun Wang, Qiegen Liu, Xianlin Song

Proceedings Volume 12768, Holography, Diffractive Optics, and Applications XIII; 127680N (2023) https://doi.org/10.1117/12.2688610
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Abstract

As an important three-dimensional (3D) display technology, holographic 3D display has great application prospects in virtual and augmented reality applications. However, it has been challenging to generate 3D hologram rapidly with high‑reconstruction quality. Here, we proposed a high-speed 3D hologram generation method via convolutional neural network (CNN). The CNN network is trained by unsupervised training, and the trained CNN can generate 3D hologram with 1024×1024 resolution at 100 planes within 60 ms. The feasibility and effectiveness of the proposed method have been demonstrated by simulation. This method will further expand the application of holographic 3D display in remote education, medical treatment, entertainment, and other fields.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiaqing Dong, Minghao Liu, Zehao Sun, Zilong Li, Xuan Liu, Wenhua Zhong, Guijun Wang, Qiegen Liu, and Xianlin Song "High-speed 3D hologram generation method via convolutional neural network", Proc. SPIE 12768, Holography, Diffractive Optics, and Applications XIII, 127680N (27 November 2023); https://doi.org/10.1117/12.2688610

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