Authors: Hyeonseung Yu, Youngrok Kim, Daeho Yang, Wontaek Seo, Yunhee Kim, Jong-Young Hong, Hoon Song, Geeyoung Sung, Younghun Sung, Sung-Wook Min, Hong-Seok Lee
Published: 2023-06-14
DOI: 10.1038/s41467-023-39329-0
Source: Full article
AbstractWhile 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.