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  • Received: Sep. 11, 2019

    Accepted: Oct. 19, 2019

    Posted: Dec. 19, 2019

    Published Online: Nov. 12, 2019

    The Author Email: Zhao Jian (jianzhao@knights.ucf.edu), Sun Yangyang (yangyang@knights.ucf.edu), Zhu Hongbo (zhbciomp@163.com), Zhu Zheyuan (zyzhu@knights.ucf.edu), Antonio-Lopez Jose E. (jealopez@creol.ucf.edu), Correa Rodrigo Amezcua (r.amezcua@creol.ucf.edu), Pang Shuo (pang@creol.ucf.edu), Schulzgen Axel (axel@creol.ucf.edu)

    DOI: 10.1117/1.AP.1.6.066001

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    Jian Zhao, Yangyang Sun, Hongbo Zhu, Zheyuan Zhu, Jose E. Antonio-Lopez, Rodrigo Amezcua Correa, Shuo Pang, Axel Schulzgen. Deep-learning cell imaging through Anderson localizing optical fiber[J]. Advanced Photonics, 2019, 1(6): 066001

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Advanced Photonics, Vol. 1, Issue 6, 066001 (2019)

Deep-learning cell imaging through Anderson localizing optical fiber

Jian Zhao1,†,*, Yangyang Sun1, Hongbo Zhu2, Zheyuan Zhu1, Jose E. Antonio-Lopez1, Rodrigo Amezcua Correa1, Shuo Pang1, and Axel Schulzgen1

Author Affiliations

  • 1University of Central Florida, CREOL, The College of Optics and Photonics, Orlando, Florida, United States
  • 2Chinese Academy of Sciences, Changchun Institute of Optics, Fine Mechanics and Physics, State Key Laboratory of Luminescence and Applications, Changchun, China

Abstract

We demonstrate a deep-learning-based fiber imaging system that can transfer real-time artifact-free cell images through a meter-long Anderson localizing optical fiber. The cell samples are illuminated by an incoherent LED light source. A deep convolutional neural network is applied to the image reconstruction process. The network training uses data generated by a setup with straight fiber at room temperature (~20 ° C) but can be utilized directly for high-fidelity reconstruction of cell images that are transported through fiber with a few degrees bend or fiber with segments heated up to 50°C. In addition, cell images located several millimeters away from the bare fiber end can be transported and recovered successfully without the assistance of distal optics. We provide evidence that the trained neural network is able to transfer its learning to recover images of cells featuring very different morphologies and classes that are never “seen” during the training process.

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