# Light Propagation Prediction through Multimode Optical Fibers with a Deep Neural Network

## TL;DR

This paper presents a deep neural network approach for accurately predicting light propagation through multimode optical fibers, using experimental data from a digital micro-mirror device and intensity measurements.

## Contribution

It introduces a novel deep learning method trained on experimental data to predict light transmission in multimode fibers with high accuracy.

## Key findings

- High prediction accuracy demonstrated by MSE, correlation coefficient, and SSIM metrics.
- Effective use of digital micro-mirror device for data collection.
- Deep neural network outperforms traditional modeling methods.

## Abstract

This work demonstrates a computational method for predicting the light propagation through a single multimode fiber using a deep neural network. The experiment for gathering training and testing data is performed with a digital micro-mirror device that enables the spatial light modulation. The modulated patterns on the device and the captured intensity-only images by the camera form the aligned data pairs. This sufficiently-trained deep neural network frame has very excellent performance for directly inferring the intensity-only output delivered though a multimode fiber. The model is validated by three standards: the mean squared error (MSE), the correlation coefficient (corr) and the structural similarity index (SSIM).

---
Source: https://tomesphere.com/paper/1812.02814