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Diffraction model-informed neural network for unsupervised layer-based computer-generated holography
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DOI：10.1364/OE.474137.png)
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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.
Keyword:
GERCHBERG-SAXTON ALGORITHM
PHASE-ONLY HOLOGRAM
IMAGE
期刊
IF:
3.3
论文数:
6.1W
被引数:
14.3W
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