基于神经网络和摄像机的轨道角动量全息术

IF 9.8 1区 物理与天体物理 Q1 OPTICS
Nima Asoudegi, Mo Mojahedi
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引用次数: 0

摘要

轨道角动量(OAM)是光的一种空间模式,在光通信和全息中作为信息载体进行加密和复用。设计OAM复用全息图的传统方法表现出次优的重建精度,因为复用通道的数量超过了标称的复用容量。由于全息显示器的光学像差和缺陷,在实际系统中重建质量进一步下降。在这项工作中,提出了两种方法,梯度下降(GD)优化和深度学习方法,用于设计OAM多路相位全息图。这两种方法都集成了Camera - In - The - Loop (CITL)校准技术,该技术可以学习一个现实的参数化传播模型来补偿系统缺陷。实验结果表明,当工作在两倍的标称OAM复用容量时,所提出的GD和神经网络方法结合CITL校准,与传统方法相比,重建图像的互相关误差分别降低了82%和58%。这些方法在实际光学系统中实现了精确、高容量和实时的OAM复用全息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Orbital Angular Momentum Holography Using Neural Network and Camera in the Loop

Orbital Angular Momentum Holography Using Neural Network and Camera in the Loop
Orbital Angular Momentum (OAM), a spatial mode of light, is employed as an information carrier for encryption and multiplexing in optical communication and holography. Conventional methods for designing OAM‐multiplexed holograms exhibit suboptimal reconstruction accuracy as the number of multiplexed channels exceeds the nominal multiplexing capacity. Reconstruction quality further degrades in practical systems due to optical aberrations and imperfections in holographic displays. In this work, two methods are proposed, a Gradient Descent (GD) optimization and a deep learning approach, for designing OAM‐multiplexed phase‐only holograms. Both methods are integrated with a Camera‐In‐The‐Loop (CITL) calibration technique that learns a realistic parameterized propagation model to compensate for system imperfections. The experimental results show that when operating at twice the nominal OAM multiplexing capacity, the proposed GD and neural network methods combined with CITL calibration, reduce cross‐correlation errors in reconstructed images by up to 82% and 58% compared to the conventional method, respectively. These methods enable accurate, high capacity, and real‐time OAM‐multiplexed holography in practical optical systems.
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来源期刊
CiteScore
14.20
自引率
5.50%
发文量
314
审稿时长
2 months
期刊介绍: Laser & Photonics Reviews is a reputable journal that publishes high-quality Reviews, original Research Articles, and Perspectives in the field of photonics and optics. It covers both theoretical and experimental aspects, including recent groundbreaking research, specific advancements, and innovative applications. As evidence of its impact and recognition, Laser & Photonics Reviews boasts a remarkable 2022 Impact Factor of 11.0, according to the Journal Citation Reports from Clarivate Analytics (2023). Moreover, it holds impressive rankings in the InCites Journal Citation Reports: in 2021, it was ranked 6th out of 101 in the field of Optics, 15th out of 161 in Applied Physics, and 12th out of 69 in Condensed Matter Physics. The journal uses the ISSN numbers 1863-8880 for print and 1863-8899 for online publications.
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