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

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.

59‐2: Invited Paper: Deep Learning‐enhanced Self‐interference Incoherent Digital Holography

“…The main difference between traditional vision data with hologram is an impulse response, which means the local signal is related to global signal and the global context affects to the local representation. In our previous work, we exploit the ResNet-based convolutional neural network (CNN) to backbone and we had to sacrifice the peripheral information due to computational cost [3]. We utilize the U-net-based image restoration model to ensure the image restoration [6].…”

“…We propose the deep learning-based SIDH filtering that can enhance the reconstruction quality of incoherent hologram. The emergence of artificial neural networks has greatly influenced the classical optics by adopting the ability of neural networks to capture the features of data [2,3]. In this paper, we introduce the principles of SIDH based on geometric phase lens, which divides the incident wave with a polarization-selective optical element.…”

Recent Applications of Optical Elements in Augmented and Virtual Reality Displays: A Review

“…Finally, the GPHOE lens is used not only in the AR/VR displays but also in the self-interference incoherent digital holographic cameras. The polarization-dependent diffraction property of the GPHOE is exploited to generate the self-interference of the incident light in those applications. − …”

Computational Optical Scanning Holography

“…In terms of holography, OSH can be categorized as incoherent digital holography (IDH) [30]. The time to obtain holograms of OSH is limited by the scanning speed of the FZP due to single-pixel detection [31], although recent IDH techniques with an image sensor can record moving targets with the framerate [32][33][34]. While OSH possesses the disadvantage of low measurement speeds, many kinds of undersampling methods have been proposed [35][36][37][38].…”