Asymmetrical neural network for real-time and high-quality computer-generated holography

Computer-generated holography based on neural network holds great promise as a real-time hologram generation method. However, existing neural network-based approaches prioritize lightweight networks to achieve real-time display, which limits their capacity for network fitting. Here, we propose an asymmetrical neural network with a non-end-to-end structure that enhances fitting capacity and delivers superior real-time display quality. The non-end-to-end structure decomposes the overall task into two sub-tasks: phase prediction and hologram encoding. The asymmetrical design tailors each sub-network to its specific sub-task using distinct basic net-layers rather than relying on similar net-layers. This method allows for a sub-network with strong feature extraction and inference capabilities to match the phase predictor, while another sub-network with efficient coding capability matches the hologram encoder. By matching network functions to tasks, our method enhances the overall network's fitting capacity while maintaining a lightweight architecture. Both numerical reconstructions and optical experiments validate the reliability and effectiveness of our proposed method.