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

Multimode fiber (MMF) imaging is an emerging field of fiber imaging technology in the last few decades. However, its high sensitivity to dynamic perturbance limits its practical applications. In this study, we propose an anti-perturbation scheme for MMF imaging based on the active measurement of the fiber configuration. We fabricate an imaging device composed of the MMF and fiber Bragg grating array to measure the MMF configuration parameters in real time and record the object–speckle pairs in different configurations for neural network training. Image reconstruction subjected to dynamic perturbations can be realized using deep learning, and the experimental results show that the introduction of fiber configuration parameters can improve the quality of anti-perturbation imaging. In addition, we realize speckle prediction using the configuration parameters and a trained neural network. The predicted speckle can be applied to flexible MMF compressive imaging. Our work proposes a new scheme for flexible MMF imaging and provides an important reference for the practical application of MMF imaging.