Share with Facebook
Tweet This
Post on reddit
Share with LinkedIn
Add to CiteULike
Add to Mendeley
Add to BibSonomy
Abstract
We propose a deep-learning-based approach to producing computer-generated holograms (CGHs) of real-world scenes. We design an end-to-end convolutional neural network (the Stereo-to-Hologram Network, SHNet) framework that takes a stereo image pair as input and efficiently synthesizes a monochromatic 3D complex hologram as output. The network is able to rapidly and straightforwardly calculate CGHs from the directly recorded images of real-world scenes, eliminating the need for time-consuming intermediate depth recovery and diffraction-based computations. We demonstrate the 3D reconstructions with clear depth cues obtained from the SHNet-based CGHs by both numerical simulations and optical holographic virtual reality display experiments.
© 2022 Optica Publishing Group
Full Article | PDF ArticleMore Like This
Chenliang Chang, Dongchen Zhu, Jiamao Li, Di Wang, Jun Xia, and Xiaolin Zhang
Opt. Lett. 47(9) 2202-2205 (2022)
Chenliang Chang, Bo Dai, Dongchen Zhu, Jiamao Li, Jun Xia, Dawei Zhang, Lianping Hou, and Songlin Zhuang
Opt. Lett. 48(4) 851-854 (2023)
Sihao Ding, Siyang Cao, Yuan F. Zheng, and Robert L. Ewing
Appl. Opt. 55(27) 7583-7592 (2016)