计算机科学
人工神经网络
全息术
网络体系结构
编码器
人工智能
推论
编码(内存)
模式识别(心理学)
光学
物理
操作系统
计算机安全
作者
Guangwei Yu,Jun Wang,Huan Yang,Zicheng Guo,Yang Wu
出处
期刊:Optics Letters
[The Optical Society]
日期:2023-09-21
卷期号:48 (20): 5351-5351
被引量:4
摘要
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.
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