4K-DMDNet: diffraction model-driven network for 4K computer-generated holography

doi:10.29026/oea.2023.220135

4K-DMDNet: diffraction model-driven network for 4K computer-generated holography

2023-01-01

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DOI：10.29026/oea.2023.220135

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Deep learning offers a novel opportunity to achieve both high-quality and high-speed computer-generated holography (CGH). Current data-driven deep learning algorithms face the challenge that the labeled training datasets limit the train-ing performance and generalization. The model-driven deep learning introduces the diffraction model into the neural net-work. It eliminates the need for the labeled training dataset and has been extensively applied to hologram generation. However, the existing model-driven deep learning algorithms face the problem of insufficient constraints. In this study, we propose a model-driven neural network capable of high-fidelity 4K computer-generated hologram generation, called 4K Diffraction Model-driven Network (4K-DMDNet). The constraint of the reconstructed images in the frequency domain is strengthened. And a network structure that combines the residual method and sub-pixel convolution method is built, which effectively enhances the fitting ability of the network for inverse problems. The generalization of the 4K-DMDNet is demonstrated with binary, grayscale and 3D images. High-quality full-color optical reconstructions of the 4K holograms have been achieved at the wavelengths of 450 nm, 520 nm, and 638 nm.

Keywords:

computer-generated holography

deep learning

model-driven neural network

sub-pixel convolution

oversampling

4K-DMDNet: diffraction model-driven network for 4K computer-generated holography

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