Using model-driven deep learning to achieve high-fidelity 4K color holographic display
In 2009, the IMAX 3D film "Avatar" swept the global film market. A few years later, the Hatsune Miku 3D concert attracted all anime fans' attention. And recently AR/VR 3D headwear devices led to a booming development of the ...
To obtain more realistic visual experiences, most of the mainstream commercial solutions for 3D display are based on the principles of binocular vision.
However, unlike the observation of real 3D objects, the depth of visual focus remains unchanged while the viewer is wearing the device to obtain 3D information. This type of vergence accommodation conflict makes the viewer susceptible to visual fatigue and vertigo, limiting the user experiences.
Computer-generated holography (CGH) can avoid the generation of vergence accommodation conflict from the origin. The experimental setups are simple and compact. CGH has received significant attention from academia and industry. It is regarded as the future form of 3D display.
In principle, CGH codes the 3D object into a digital two-dimensional (2D) hologram based on diffractive calculations. And then the 2D hologram is uploaded to a spatial light modulator (SLM) illuminated by plane waves. The optical reconstruction of the 3D object is obtained at a certain distance. CGH has potential applications in a wide range of 3D displays such as head-mounted displays, heads-up displays, and projection displays.
How to generate high-speed and high-quality 2D holograms is a key issue and essential research direction in this field at present.
Fig. 1 Generation and reconstruction processes of 4K holograms by the 4K-DMDNet. Credit: Opto-Electronic Advances (2023). DOI: 10.29026/oea.2023.220135
Fig. 2 Comparison of (a) data-driven deep learning with (b) 4K-DMDNet in terms of the training principle. Credit: Opto-Electronic Advances (2023). DOI: 10.29026/oea.2023.220135
Fig. 3 Optical reconstructions of different types of images: (a) color image and (b) binary image. Credit: Opto-Electronic Advances (2023). DOI: 10.29026/oea.2023.220135