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

Here, the authors demonstrate high-speed imaging through multimode optical fibers by using the high intermodal dispersion to transform 2D spatial information into 1D temporal pulsed signal streams. Deep learning is used to reconstruct the images of micron-scale objects at high frame rates. 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.

Automated summary

This paper presents an all-fiber high-speed imaging method that converts two-dimensional spatial information into one-dimensional temporal pulse streams using high intermodal dispersion. The images of micron-scale objects are reconstructed at high frame rates using deep learning. This approach combines high speeds, mechanical flexibility, and integration and has important implications for researching various phenomena in vivo.