Opt Express. 2023 Feb 27;31(5):7413-7424. doi: 10.1364/OE.483590.
Multi-plane reconstruction is essential for realizing a holographic three-dimensional (3D) display. One fundamental issue in conventional multi-plane Gerchberg-Saxton (GS) algorithm is the inter-plane crosstalk, mainly caused by the neglect of other planes' interference in the process of amplitude replacement at each object plane. In this paper, we proposed the time-multiplexing stochastic gradient descent (TM-SGD) optimization algorithm to reduce the multi-plane reconstruction crosstalk. First, the global optimization feature of stochastic gradient descent (SGD) was utilized to reduce the inter-plane crosstalk. However, the crosstalk optimization effect would degrade as the number of object planes increases, due to the imbalance between input and output information. Thus, we further introduced the time-multiplexing strategy into both the iteration and reconstruction process of multi-plane SGD to increase input information. In TM-SGD, multiple sub-holograms are obtained through multi-loop iteration and then sequentially refreshed on spatial light modulator (SLM). The optimization condition between the holograms and the object planes converts from one-to-many to many-to-many, improving the optimization of inter-plane crosstalk. During the persistence of vision, multiple sub-hologram jointly reconstruct the crosstalk-free multi-plane images. Through simulation and experiment, we confirmed that TM-SGD could effectively reduce the inter-plane crosstalk and improve image quality.The proposed TM-SGD-based holographic display has wide applications in tomographic 3D visualization for biology, medical science, and engineering design, which need to reconstruct multiple independent tomographic images without inter-plane crosstalk.
多平面重建对于实现全息三维(3D)显示至关重要。传统多平面 Gerchberg-Saxton(GS)算法的一个基本问题是平面间串扰,主要是由于在每个物平面的幅度替换过程中忽略了其他平面的干扰。在本文中,我们提出了时间复用随机梯度下降(TM-SGD)优化算法来减少多平面重建串扰。首先,利用随机梯度下降(SGD)的全局优化特性来减少平面间串扰。然而,由于输入和输出信息之间的不平衡,随着物平面数量的增加,串扰优化效果会降低。因此,我们进一步将时间复用策略引入多平面 SGD 的迭代和重建过程中,以增加输入信息。在 TM-SGD 中,通过多轮迭代获得多个子全息图,然后顺序刷新空间光调制器(SLM)。全息图和物平面之间的优化条件从一对多转换为多对多,从而改善了平面间串扰的优化。在视觉暂留期间,多个子全息图共同重建无串扰的多平面图像。通过仿真和实验,我们证实了 TM-SGD 可以有效地减少平面间串扰并提高图像质量。所提出的基于 TM-SGD 的全息显示在生物学、医学科学和工程设计等领域的层析 3D 可视化中有广泛的应用,这些领域需要重建多个独立的层析图像而没有平面间串扰。