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

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