Deep learning-assisted light sheet holography

被引:1
|
作者
Asoudegi, Nima [1 ]
Dorrah, Ahmed h. [2 ]
Mojahedi, Mo [1 ]
机构
[1] Univ Toronto, Dept Elect & Comp Engn, Toronto, ON M5S 3G4, Canada
[2] Harvard Univ, Harvard John A Paulson Sch Engn & Appl Sci, Cambridge, MA 02138 USA
基金
加拿大自然科学与工程研究理事会;
关键词
COMPUTER-GENERATED HOLOGRAPHY; CROSSTALK; ALGORITHM; PHASE;
D O I
10.1364/OE.505627
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
In a novel approach to layer -based holography, we propose a machine learningassisted light sheet holography-an optimized holography technique which projects a target scene onto sheets of light along the longitudinal planes (i.e. planes perpendicular to the plane of the hologram). Using a convolutional neural network in conjunction with superposition of Bessel beams, we generate high -definition images which can be stacked in parallel onto longitudinal planes with very high fidelity. Our holography system provides high axial resolution and excellent control over the light intensity along the optical path, which is suitable for augmented reality and/or virtual reality applications. (c) 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
引用
收藏
页码:1161 / 1175
页数:15
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