4K-DMDNet: diffraction model-driven network for 4K computer-generated holography
4K DMDNet：用于4K计算机生成全息术的衍射模型驱动网络
4K DMDNet：4 Kコンピュータ生成ホログラフィーのための回折モデル駆動ネットワーク
4K DMDNet: 4K 컴퓨터 생성 홀로그램을 위한 회절 모델 제어 네트워크
4k dmdnet: red impulsada por modelos difractivos para una Holografía generada por computadora 4K
4K dmdnet: Réseau piloté par modèle de diffraction pour l'holographie 4K générée par ordinateur
4K DMDNet: сеть с дифракционной моделью для генерации голографии на 4K - компьютерах
Kexuan Liu 刘珂瑄, Jiachen Wu 吴佳琛, Zehao He 何泽浩, Liangcai Cao 曹良才
State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments, Tsinghua University, Beijing 100084, China
中国 北京 清华大学精密仪器系 精密测试技术及仪器国家重点实验室
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 training performance and generalization.
The model-driven deep learning introduces the diffraction model into the neural network. 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.
