Towards holographic streaming systems: From real-time acquisition of the real world to holographic display

Abstract While recent progress in holographic displays has shown that they are capable of displaying photorealistic 3D holograms in real time, the acquisition of high-quality real-world holograms has remained a missing piece regarding the realization of holographic streaming systems. Incoherent holographic cameras, which record holograms under daylight conditions, are suitable candidates for real-world acquisition, as they avoid the safety issue involved in the use of laser lights; however, they 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 the first holographic streaming system integrating a holographic camera and a holographic display, with the aim of developing the ultimate holographic ecosystem of the future.