Journal papers
Nathan Tessema Ersaro; Cem Yalcin; Liz Murray; Leyla Kabuli; Laura Waller; Rikky Muller
Fast non-iterative algorithm for 3D point-cloud holography Journal Article
In: Opt. Express, vol. 31, no. 22, pp. 36468–36485, 2023.
Abstract | Links | BibTeX | Tags: Diode pumped lasers; Fast Fourier transforms; Image quality; Phase retrieval; Spatial light modulators; Three dimensional imaging
@article{Ersaro:23,
title = {Fast non-iterative algorithm for 3D point-cloud holography},
author = {Nathan Tessema Ersaro and Cem Yalcin and Liz Murray and Leyla Kabuli and Laura Waller and Rikky Muller},
url = {https://opg.optica.org/oe/abstract.cfm?URI=oe-31-22-36468},
doi = {10.1364/OE.498302},
year = {2023},
date = {2023-10-01},
urldate = {2023-10-01},
journal = {Opt. Express},
volume = {31},
number = {22},
pages = {36468--36485},
publisher = {Optica Publishing Group},
abstract = {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.},
keywords = {Diode pumped lasers; Fast Fourier transforms; Image quality; Phase retrieval; Spatial light modulators; Three dimensional imaging},
pubstate = {published},
tppubtype = {article}
}
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.