Memory‐Reduced Convolutional Neural Network for Fast Phase Hologram Generation

Computer‐generated holography (CGH) has advanced the development of human‐centric holographic near‐eye displays. Recent work has proposed an end‐to‐end convolutional neural network that converts 2D images into 3D holograms, enabling real‐time 3D holographic displays from widespread available images. However, the high computational cost of deep learning‐based methods limits their practical application. Deploying such algorithms on resource‐constrained mobile platforms requires more efficient models with reduced computational memory and power demands, which plays a crucial role in promoting human‐centric virtual reality/augmented reality displays. In this article, a lightweight 3D hologram generation model is proposed using neural network quantization from the input of single 2D image. Specifically, a 2D‐to‐3D CGH model is quantized from 32‐bit floating‐point to 8‐bit integer precision. The results show that the INT8 model reduces size by 60%, improves processing speed by a factor of three, and achieves comparable hologram quality to the FP32 model. This work enables the practical deployment of 2D‐to‐3D CGH model on low‐power platforms, bridging the gap between high‐performance holographic computation and real‐world wearable display systems.