Diffraction model-driven neural network with semi-supervised training strategy for real-world 3D holographic photography

Compared to traditional 2D displays, 3D display technology provides richer information to the viewer. Learning-based computer-generated holography (CGH) has shown great potential in realizing real-time holographic 3D displays. However, most of the current learning-based CGH algorithms cannot quickly complete the training stage and produce high-quality holograms due to insufficient constraints in the training stage of the neural network. In this paper, we propose a diffractive model-driven neural network trained using a semi-supervised training (SST-holo) strategy and incorporate a state-of-the-art monocular depth estimation algorithm to achieve the fast generation of holograms of real-world 3D scenes. Compared to the supervised training strategy, our proposed semi-supervised training strategy does not require high-quality labeled datasets, but can significantly improve the imaging quality and generalization of the algorithm. Incorporating the Res-MSR block in SST-holo to adaptively learn image features of different scales enhances the learning capability of the network. In addition, we adopt a random splicing processing strategy to preprocess the dataset to ensure that the original features in the dataset are not corrupted. SST-holo can generate high-quality 3D phase-only holograms with 2 K resolution in 0.015 seconds. Both monochrome and color optical experiments show that the proposed algorithm has good 3D effect and generalization ability and can effectively improve the quality of reconstructed images.