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Transmission of natural scene images through a multimode fibre

Author

Listed:
  • Piergiorgio Caramazza

    (University of Glasgow)

  • Oisín Moran

    (University of Glasgow)

  • Roderick Murray-Smith

    (University of Glasgow)

  • Daniele Faccio

    (University of Glasgow)

Abstract

The optical transport of images through a multimode fibre remains an outstanding challenge with applications ranging from optical communications to neuro-imaging. State of the art approaches either involve measurement and control of the full complex field transmitted through the fibre or, more recently, training of artificial neural networks that however, are typically limited to image classes belong to the same class as the training data set. Here we implement a method that statistically reconstructs the inverse transformation matrix for the fibre. We demonstrate imaging at high frame rates, high resolutions and in full colour of natural scenes, thus demonstrating general-purpose imaging capability. Real-time imaging over long fibre lengths opens alternative routes to exploitation for example for secure communication systems, novel remote imaging devices, quantum state control processing and endoscopy.

Suggested Citation

  • Piergiorgio Caramazza & Oisín Moran & Roderick Murray-Smith & Daniele Faccio, 2019. "Transmission of natural scene images through a multimode fibre," Nature Communications, Nature, vol. 10(1), pages 1-6, December.
  • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10057-8
    DOI: 10.1038/s41467-019-10057-8
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    Cited by:

    1. Zhoutian Liu & Lele Wang & Yuan Meng & Tiantian He & Sifeng He & Yousi Yang & Liuyue Wang & Jiading Tian & Dan Li & Ping Yan & Mali Gong & Qiang Liu & Qirong Xiao, 2022. "All-fiber high-speed image detection enabled by deep learning," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    2. Bo Dai & Liang Zhang & Chenglong Zhao & Hunter Bachman & Ryan Becker & John Mai & Ziao Jiao & Wei Li & Lulu Zheng & Xinjun Wan & Tony Jun Huang & Songlin Zhuang & Dawei Zhang, 2021. "Biomimetic apposition compound eye fabricated using microfluidic-assisted 3D printing," Nature Communications, Nature, vol. 12(1), pages 1-11, December.

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