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通过具有实值强度传输矩阵的多模光纤进行透视。

Seeing through multimode fibers with real-valued intensity transmission matrices.

作者信息

Zhao Tianrui, Ourselin Sebastien, Vercauteren Tom, Xia Wenfeng

出版信息

Opt Express. 2020 Jul 6;28(14):20978-20991. doi: 10.1364/OE.396734.

DOI:10.1364/OE.396734
PMID:32680147
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7470672/
Abstract

Image transmission through multimode optical fibers has been an area of immense interests driven by the demand for miniature endoscopes in biomedicine and higher speed and capacity in telecommunications. Conventionally, a complex-valued transmission matrix is obtained experimentally to link the input and output light fields of a multimode fiber for image retrieval, which complicates the experimental setup and increases the computational complexity. Here, we report a simple and high-speed method for image retrieval based on our demonstration of a pseudo-linearity between the input and output light intensity distributions of multimode fibers. We studied the impact of several key parameters to image retrieval, including image pixel count, fiber core diameter and numerical aperture. We further demonstrated with experiments and numerical simulations that a wide variety of input binary and gray scale images could be faithfully retrieved from the corresponding output speckle patterns. Thus, it promises to be useful for highly miniaturized endoscopy in biomedicine and spatial-mode-division multiplexing in telecommunications.

摘要

通过多模光纤进行图像传输一直是一个备受关注的领域,这是由生物医学中对微型内窥镜的需求以及电信领域对更高速度和容量的需求所驱动的。传统上,通过实验获得一个复值传输矩阵,以关联多模光纤的输入和输出光场来进行图像检索,这使得实验装置变得复杂,并增加了计算复杂度。在此,我们基于对多模光纤输入和输出光强分布之间伪线性的证明,报告一种简单且高速的图像检索方法。我们研究了几个关键参数对图像检索的影响,包括图像像素数量、光纤芯径和数值孔径。我们通过实验和数值模拟进一步证明,可以从相应的输出散斑图案中准确地检索出各种输入的二值和灰度图像。因此,它有望在生物医学中的高度微型化内窥镜检查以及电信中的空间模式分割复用方面发挥作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92d9/7470672/1fff11cded06/oe-28-14-20978-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92d9/7470672/23f4b5f85cd2/oe-28-14-20978-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92d9/7470672/78a9446d833f/oe-28-14-20978-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92d9/7470672/faa4a17a7535/oe-28-14-20978-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92d9/7470672/875f6d5d8ecf/oe-28-14-20978-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92d9/7470672/d1805c512437/oe-28-14-20978-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92d9/7470672/1052ddcad4d7/oe-28-14-20978-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92d9/7470672/c28c3bca0d06/oe-28-14-20978-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92d9/7470672/7e887883caf6/oe-28-14-20978-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92d9/7470672/1fff11cded06/oe-28-14-20978-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92d9/7470672/23f4b5f85cd2/oe-28-14-20978-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92d9/7470672/78a9446d833f/oe-28-14-20978-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92d9/7470672/faa4a17a7535/oe-28-14-20978-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92d9/7470672/875f6d5d8ecf/oe-28-14-20978-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92d9/7470672/d1805c512437/oe-28-14-20978-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92d9/7470672/1052ddcad4d7/oe-28-14-20978-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92d9/7470672/c28c3bca0d06/oe-28-14-20978-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92d9/7470672/7e887883caf6/oe-28-14-20978-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92d9/7470672/1fff11cded06/oe-28-14-20978-g009.jpg

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Deep learning the high variability and randomness inside multimode fibers.深度学习多模光纤内部的高可变性和随机性。
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