Anti-perturbation Multimode Fiber Imaging Based on the Active Measurement of the Fiber Configuration

Multimode fiber (MMF) imaging is an emerging field offiber imagingtechnology in the last few decades. However, its high sensitivityto dynamic perturbance limits its practical applications. In thisstudy, we propose an anti-perturbation scheme for MMF imaging basedon the active measurement of the fiber configuration. We fabricatean imaging device composed of the MMF and fiber Bragg grating arrayto measure the MMF configuration parameters in real time and recordthe object-speckle pairs in different configurations for neuralnetwork training. Image reconstruction subjected to dynamic perturbationscan be realized using deep learning, and the experimental resultsshow that the introduction of fiber configuration parameters can improvethe quality of anti-perturbation imaging. In addition, we realizespeckle prediction using the configuration parameters and a trainedneural network. The predicted speckle can be applied to flexible MMFcompressive imaging. Our work proposes a new scheme for flexible MMFimaging and provides an important reference for the practical applicationof MMF imaging.

Automated summary

Multimode fiber (MMF) imaging is a new field of fiber imaging technology that has emerged in the last few decades. However, its sensitivity to dynamic perturbance limits its practical applications. In this study, an anti-perturbation scheme for MMF imaging based on active measurement of the fiber configuration is proposed. Experimental results show that the introduction of fiber configuration parameters can improve the quality of anti-perturbation imaging. Additionally, speckle prediction using the configuration parameters and a trained neural network is realized.