Deep learning-based incoherent holographic camera enabling acquisition of real-world holograms for holographic streaming systemopen access
- Authors
- Yu, Hyeonseung; Kim, Youngrok; Yang, Daeho; Seo, Wontaek; Kim, Yunhee; Hong, Jong-Young; Song, Hoon; Sung, Geeyoung; Sung, Younghun; Min, Sung-Wook; Lee, Hong-Seok
- Issue Date
- Jun-2023
- Publisher
- NATURE PORTFOLIO
- Citation
- NATURE COMMUNICATIONS, v.14, no.1
- Journal Title
- NATURE COMMUNICATIONS
- Volume
- 14
- Number
- 1
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/89105
- DOI
- 10.1038/s41467-023-39329-0
- ISSN
- 2041-1723
- 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. The authors develop a deep learning-based incoherent holographic camera system in order to deliver visually enhanced holograms in real-time. The neural network filters the noise in the captured holograms, and by integrating a holographic camera and a display, they demonstrate a holographic streaming system.
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