Deep Learning for Estimating Deflection Direction of a Multimode Fiber From Specklegram

This study presents a novel approach and proof-of-concept for measuring the deflection direction of a multimode fiber's tip through analysis of the shape and structure of speckle patterns. First, we utilize a pendulum-based apparatus to construct a comprehensive dataset for studying the association between speckle patterns and the deflection of a multimode fiber tip. Then, we train a convolutional neural network (CNN) model to learn the relationship between a fiber optic deformation parameter and the variations in the shape and structure of speckle patterns. Finally, the ability of the model in estimating the deflection direction is evaluated by using unseen images from the test set. Our experimental results show that speckle patterns provide a feasible solution for measuring the deflection direction of the multimode fiber's tip. Moreover, our results provide insights into the generalization capabilities of speckle patterns analysis for sensing the direction of deflection.

Automated summary

This study proposes a novel approach to measuring the deflection direction of a multimode fiber's tip using speckle patterns analysis and a convolutional neural network model. Experimental results demonstrate the feasibility of using speckle patterns for this purpose. The study also sheds light on the generalization capabilities of speckle patterns analysis in sensing the direction of deflection.