Liu Ninghe, Liu Kexuan, Yang Yixin, Peng Yifan, Cao Liangcai
Weiyang College, Tsinghua University, Beijing, 100084, China.
Department of Precision Instrument, Tsinghua University, Beijing, 100084, China.
Nat Commun. 2025 Aug 20;16(1):7761. doi: 10.1038/s41467-025-62997-z.
Computer-generated holography (CGH) offers a promising method to create true-to-life reconstructions of objects. While recent advances in deep learning-based CGH algorithms have significantly improved the tradeoff between algorithm runtime and image quality, most existing models are restricted to a fixed propagation distance, limiting their adaptability in practical applications. Here, we present a deep learning-based algorithmic CGH solver that achieves propagation-adaptive CGH synthesis using a spatial and Fourier neural operator (SFO-solver). Grounded in two physical insights of optical diffraction, specifically its global information flow and the circular symmetry, SFO-solver encodes both target intensity and propagation distance as network inputs with enhanced physical interpretability. The method enables high-speed 4 K CGH synthesis at 0.16 seconds per frame, delivering an average PSNR of 39.25 dB across a 30 mm depth range. We experimentally demonstrate various-depth 2D holographic projection and an adjustable multi-plane 3D display without requiring hardware modifications. SFO-solver showcases significant improvements in the flexibility of deep learning-based CGH synthesis and provides a scalable foundation to fulfill broader user-oriented requirements such as dynamic refocusing and interactive holographic display.
计算机生成全息术（CGH）提供了一种创建逼真物体重建的有前景的方法。虽然基于深度学习的CGH算法的最新进展显著改善了算法运行时间和图像质量之间的权衡，但大多数现有模型仅限于固定的传播距离，限制了它们在实际应用中的适应性。在这里，我们提出了一种基于深度学习的算法CGH求解器，它使用空间和傅里叶神经算子（SFO求解器）实现传播自适应CGH合成。基于光学衍射的两个物理见解，特别是其全局信息流和圆对称性，SFO求解器将目标强度和传播距离都编码为网络输入，具有增强的物理可解释性。该方法能够以每帧0.16秒的速度进行高速4K CGH合成，在30毫米深度范围内平均PSNR为39.25dB。我们通过实验展示了各种深度的2D全息投影和可调节的多平面3D显示，而无需硬件修改。SFO求解器在基于深度学习的CGH合成的灵活性方面展示了显著改进，并为满足更广泛的面向用户的需求（如动态重新聚焦和交互式全息显示）提供了可扩展的基础。
