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.

We propose a compressive self-interference incoherent digital holography (SIDH) with a geometric phase metalens for section-wise holographic object reconstruction. We specify the details of the SIDH with a geometric phase metalens design that covers the visible wavelength band, analyze a spatial distortion problem in the SIDH and address a process of a compressive holographic section-wise reconstruction with analytic spatial calibration. The metalens allows us to realize a compressive SIDH system in the visible wavelength band using an image sensor with relatively low bandwidth. The operation of the proposed compressive SIDH is verified through numerical simulations.

We propose a phase-assisted camera-in-the-loop optimization technique for holographic displays to enhance the quality of phase representation. Phase-only hologram is one of the most popular methods for three-dimensional hologram reproduction. However, the optical noise resulting from imperfections in the optical configuration, including the light source, the relay optics, and the spatial light modulator, affects the reconstructed hologram quality. We focus on constant noise terms that arise during the phase representation process in a phase-only spatial light modulator (PSLM). To estimate these noise terms, we employ a polarization-based 4-f system to acquire the constant complex term at the PSLM surface and the Fourier spectrum. Moreover, we use a random phase at iteration to effectively isolate and extract common noise components. Based on the extracted noise, we optimize the initial hologram using the modified angular spectrum propagator, taking into account the actual noise presented in the PSLM. Experimental results validate the feasibility of the proposed method, demonstrating that a single noise estimation significantly reduces speckle and improves image quality in multi-depth holograms.

Incoherent digital holography is a promising solution for acquiring three-dimensional information in the form of interference without a laser. Self-interference is the principal phenomenon of incoherent holography which splits the incident light wave into two waves and interferes with each one. The characteristics of incoherent holograms are defined by the optical configuration of wavefront dividing units. In this paper, we propose the synthetic aperture method to achieve super-resolution of geometric phase lens-based self-interference incoherent digital holography. We analyze the resolution of incoherent holograms acquired from geometric phase lenses and experimentally demonstrate the resolution enhancement via the synthetic-aperture method. Moreover, the sparse synthetic-aperture method is proposed to ensure effective data capturing and the numerical optimization method is also proposed to fill the gap between the empty space of sparsely acquired holograms.

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.