Fast non-iterative algorithm for 3D point-cloud holography

被引：3

作者：

Ersaro N.T. ^{[1
]}

Yalcin C. ^{[2
]}

Murray L. ^{[2
]}

Kabuli L. ^{[2
]}

Waller L. ^{[1
,2
]}

Muller R. ^{[1
,2
]}

机构：

[1] Graduate Program in Bioengineering, University of California, Berkeley and University of California, San Francisco, Berkeley, 94720, CA

[2] Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, 94720, CA

来源：

Optics Express | 2023年 / 31卷 / 22期

基金：

美国国家科学基金会; 美国国家卫生研究院;

关键词：

Computer generated holography - Deep learning - Electron holography - Light modulators - Pixels;

D O I：

10.1364/OE.498302

中图分类号：

学科分类号：

摘要：

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. © 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

引用

页码：36468 / 36485

页数：17