Deep learning-based incoherent holographic camera enabling acquisition of real-world holograms for holographic streaming system

Hyeonseung Yu, Youngrok Kim, Daeho Yang, Wontaek Seo, Yunhee Kim, Jong-Young Hong, Hoon Song, Geeyoung Sung, Younghun Sung, Sung-Wook Min () and Hong-Seok Lee ()
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Hyeonseung Yu: Samsung Electronics
Youngrok Kim: KyungHee University
Daeho Yang: Samsung Electronics
Wontaek Seo: Samsung Electronics
Yunhee Kim: Samsung Electronics
Jong-Young Hong: Samsung Electronics
Hoon Song: Samsung Electronics
Geeyoung Sung: Samsung Electronics
Younghun Sung: Samsung Electronics
Sung-Wook Min: KyungHee University
Hong-Seok Lee: Seoul National University

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: Abstract While recent research has shown that holographic displays can represent photorealistic 3D holograms in real time, the difficulty in acquiring high-quality real-world holograms has limited the realization of holographic streaming systems. Incoherent holographic cameras, which record holograms under daylight conditions, are suitable candidates for real-world acquisition, as they prevent the safety issues associated with the use of lasers; however, these cameras are hindered by severe noise due to the optical imperfections of such systems. In this work, we develop a deep learning-based incoherent holographic camera system that can deliver visually enhanced holograms in real time. A neural network filters the noise in the captured holograms, maintaining a complex-valued hologram format throughout the whole process. Enabled by the computational efficiency of the proposed filtering strategy, we demonstrate a holographic streaming system integrating a holographic camera and holographic display, with the aim of developing the ultimate holographic ecosystem of the future.

Date: 2023
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DOI: 10.1038/s41467-023-39329-0

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