All-fiber high-speed image detection enabled by deep learning

Zhoutian Liu, Lele Wang, Yuan Meng, Tiantian He, Sifeng He, Yousi Yang, Liuyue Wang, Jiading Tian, Dan Li, Ping Yan, Mali Gong, Qiang Liu and Qirong Xiao ()
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Zhoutian Liu: Tsinghua University
Lele Wang: Tsinghua University
Yuan Meng: Tsinghua University
Tiantian He: Tsinghua University
Sifeng He: Tsinghua University
Yousi Yang: Tsinghua University
Liuyue Wang: Tsinghua University
Jiading Tian: Tsinghua University
Dan Li: Tsinghua University
Ping Yan: Tsinghua University
Mali Gong: Tsinghua University
Qiang Liu: Tsinghua University
Qirong Xiao: Tsinghua University

Nature Communications, 2022, vol. 13, issue 1, 1-8

Abstract: Abstract Ultra-high-speed imaging serves as a foundation for modern science. While in biomedicine, optical-fiber-based endoscopy is often required for in vivo applications, the combination of high speed with the fiber endoscopy, which is vital for exploring transient biomedical phenomena, still confronts some challenges. We propose all-fiber imaging at high speeds, which is achieved based on the transformation of two-dimensional spatial information into one-dimensional temporal pulsed streams by leveraging high intermodal dispersion in a multimode fiber. Neural networks are trained to reconstruct images from the temporal waveforms. It can not only detect content-aware images with high quality, but also detect images of different kinds from the training images with slightly reduced quality. The fiber probe can detect micron-scale objects with a high frame rate (15.4 Mfps) and large frame depth (10,000). This scheme combines high speeds with high mechanical flexibility and integration and may stimulate future research exploring various phenomena in vivo.

Date: 2022
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DOI: 10.1038/s41467-022-29178-8

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