Dong Zhenxing, Ling Yuye, Li Yan, Su Yikai
Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
State Key Lab of Advanced Optical Communication Systems and Networks, Shanghai Jiao Tong University, Shanghai 200240, China.
Sci Adv. 2025 Jan 31;11(5):eads9876. doi: 10.1126/sciadv.ads9876. Epub 2025 Jan 29.
Holography is capable of rendering three-dimensional scenes with full-depth control and delivering transformative experiences across numerous domains, including virtual and augmented reality, education, and communication. However, traditional holography presents 3D scenes with unnatural defocus and severe speckles due to the limited space bandwidth product of the spatial light modulator (SLM). Here, we introduce Motion Hologram, a holographic technique that accurately portrays photorealistic and speckle-free 3D scenes. This technique leverages a single hologram and a learnable motion trajectory, which are jointly optimized within a deep reinforcement learning framework. Specifically, we experimentally demonstrated that the proposed technique could achieve a 4- to 5-dB PSNR improvement of focal stacks in comparison with traditional holography and could successfully depict speckle-free, high-fidelity, and full-color 3D displays using only a commercial SLM. We believe that the proposed method promises a prospective form of holographic displays that will offer immersive viewing experiences for audiences.
全息术能够通过全深度控制呈现三维场景，并在包括虚拟现实和增强现实、教育及通信在内的众多领域提供变革性体验。然而，由于空间光调制器（SLM）有限的空间带宽积，传统全息术呈现的三维场景存在不自然的散焦和严重的散斑。在此，我们介绍运动全息图，这是一种能精确描绘逼真且无散斑三维场景的全息技术。该技术利用单个全息图和可学习的运动轨迹，在深度强化学习框架内进行联合优化。具体而言，我们通过实验证明，与传统全息术相比，所提出的技术能够使焦堆栈的峰值信噪比提高4至5分贝，并且仅使用商用SLM就能成功描绘无散斑、高保真和全彩色的三维显示器。我们相信，所提出的方法有望成为一种全息显示形式，为观众提供沉浸式观看体验。
