Ersaro Nathan Tessema, Yalcin Cem, Murray Liz, Kabuli Leyla, Waller Laura, Muller Rikky
Opt Express. 2023 Oct 23;31(22):36468-36485. doi: 10.1364/OE.498302.
Recently developed iterative and deep learning-based approaches to computer-generated holography (CGH) have been shown to achieve high-quality photorealistic 3D images with spatial light modulators. However, such approaches remain overly cumbersome for patterning sparse collections of target points across a photoresponsive volume in applications including biological microscopy and material processing. Specifically, in addition to requiring heavy computation that cannot accommodate real-time operation in mobile or hardware-light settings, existing sampling-dependent 3D CGH methods preclude the ability to place target points with arbitrary precision, limiting accessible depths to a handful of planes. Accordingly, we present a non-iterative point cloud holography algorithm that employs fast deterministic calculations in order to efficiently allocate patches of SLM pixels to different target points in the 3D volume and spread the patterning of all points across multiple time frames. Compared to a matched-performance implementation of the iterative Gerchberg-Saxton algorithm, our algorithm's relative computation speed advantage was found to increase with SLM pixel count, reaching >100,000x at 512 × 512 array format.
最近开发的基于迭代和深度学习的计算机生成全息术（CGH）方法已被证明能够通过空间光调制器实现高质量的逼真3D图像。然而，在包括生物显微镜和材料加工在内的应用中，对于在光响应体积内对稀疏目标点集合进行图案化处理而言，此类方法仍然过于繁琐。具体而言，除了需要大量计算（无法在移动或硬件轻量化环境中进行实时操作）之外，现有的依赖采样的3D CGH方法无法以任意精度放置目标点，将可访问深度限制在少数几个平面上。因此，我们提出了一种非迭代点云全息算法，该算法采用快速确定性计算，以便有效地将空间光调制器（SLM）像素块分配给3D体积中的不同目标点，并在多个时间帧上分布所有点的图案化处理。与迭代格尔奇贝格-萨克斯顿算法的匹配性能实现相比，我们发现随着SLM像素数量的增加，我们算法的相对计算速度优势会增大，在512×512阵列格式下达到>100,000倍。
