Deep learning the high variability and randomness inside multimode fibers.

Pengfei Fan,Lei Su,Tianrui Zhao

doi:10.1364/oe.27.020241

Abstract

Multimode fibers (MMF) are remarkable high-capacity information channels. However, the MMF transmission is highly sensitive to external perturbations and environmental changes. Here, we show the successful binary image transmission using deep learning through a single MMF subject to dynamic shape variations. As a proof-of-concept experiment, we find that a convolutional neural network has excellent generalization capability with various MMF transmission states to accurately predict unknown information at the other end of the MMF at any of these states. Our results demonstrate that deep learning is a promising solution to address the high variability and randomness challenge of MMF based information channels. This deep-learning approach is the starting point of developing future high-capacity MMF optical systems and devices and is applicable to optical systems concerning other diffusing media.

Highlights

Multimode fibers (MMF) have recently attracted significant renewed interest in applications such as optical communication, imaging and optical trapping [1,2,3,4,5,6,7,8,9,10,11,12]
The measured transmission matrices (TM) is verified experimentally to have an average accuracy of 98% for output speckle pattern calculation
In conclusion, our findings suggest that the high variability and randomness inside MMFs can be overcome by training a deep neural network with the possible variations that may occur to a certain MMF based optical system

Summary

Introduction

Multimode fibers (MMF) have recently attracted significant renewed interest in applications such as optical communication, imaging and optical trapping [1,2,3,4,5,6,7,8,9,10,11,12]. Individual spatial modes or mode groups in a single MMF were proposed as separate information channels This can be combined with other optical communication multiplexing technologies such as wavelength division multiplexing (WDM), to break the transmission limit of a single optical fiber. Another attractive application is an ultrathin single MMF endoscope based on the large number of fiber modes to perform high-resolution in vivo imaging. Whilst the TM-based image reconstruction approach has excellent capability to reconstruct a complex image, a TM is only applicable to the transmission state in which it is calibrated This unavoidably fails at the image recovery if the MMF transmission channel has changed. This largely limits the practical application of MMFs and hinders the full exploitation of their information capacity

Methods

Results

Conclusion