Neural Network-Based Multimode Fiber Imaging and Characterization Under Thermal Perturbations

TL;DR

This paper demonstrates that neural network-based multimode fiber imaging remains robust under thermal perturbations, enabling accurate image reconstruction despite environmental changes, and introduces methods to understand fiber properties via NN parameters.

Contribution

The study introduces a neural network approach for MMF imaging that maintains robustness under thermal changes and uses NN parameters to infer fiber transmission properties.

Findings

Neural networks can reconstruct images through MMF despite temperature variations up to 50°C.

A dense neural network outperforms convolutional neural networks in this task.

NN parameters can approximate the fiber's transmission matrices and relate to temperature-dependent properties.

Abstract

Multimode fiber (MMF) imaging aided by machine learning holds promise for numerous applications, including medical endoscopy. A key challenge for this technology is the sensitivity of modal transmission characteristics to environmental perturbations. Here, we show experimentally that an MMF imaging scheme based on a neural network (NN) can achieve results that are significantly robust to thermal perturbations. For example, natural images are successfully reconstructed as the MMF's temperature is varied by up to 50$^{\circ}$C relative to the training scenario, despite substantial variations in the speckle patterns caused by thermal changes. A dense NN with a single hidden layer is found to outperform a convolutional NN suitable for standard computer vision tasks. In addition, we demonstrate that NN parameters can be used to understand the MMF properties by reconstructing the approximate transmission matrices, and we show that the image reconstruction accuracy is directly related to the temperature dependence of the MMF's transmission characteristics.