Diffraction model-informed neural network for unsupervised layer-based computer-generated holography

PLUTO / PLUTO-2 Spatial Light Modulators
Deep Learning / Neuronal Network Digital-/ Computer Holography/ CGH
Published on:
Authors: Shui, Xinghua; Zheng, Huadong; Xia, Xinxing; Yang, Furong; Wang, Weisen & Yu, Yingjie
Abstract:
“Learning-based computer-generated holography (CGH) has shown remarkable promise to enable real-time holographic displays. Supervised CGH requires creating a large-scale dataset with target images and corresponding holograms. We propose a diffraction model-informed neural network framework (self-holo) for 3D phase-only hologram generation. Due to the angular spectrum propagation being incorporated into the neural network, the self-holo can be trained in an unsupervised manner without the need of a labeled dataset. Utilizing the various representations of a 3D object and randomly reconstructing the hologram to one layer of a 3D object keeps the complexity of the self-holo independent of the number of depth layers. The self-holo takes amplitude and depth map images as input and synthesizes a 3D hologram or a 2D hologram. We demonstrate 3D reconstructions with a good 3D effect and the generalizability of self-holo in numerical and optical experiments.”

Open Access

Publication: Optics Express
Issue/Year: Optics Express, Volume 30; Number 25; Pages 44814; 2022
DOI: 10.1364/oe.474137
Link: https://doi.org/10.1364/oe.474137

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