Work

High-fidelity image transmission and reconstruction through multimode fiber using OAM modes and deep learning

Year: 2026

Type: article

Abstract: Multimode optical fibers, with their high spatial-mode capacity, present a promising platform for high-fidelity image transmission. However, environmental instabilities and complex input patterns often result in intricate speckle outputs, complicating accurate image reconstruction. In this work, we encode grayscale images through orbital angular momentum (OAM) mode superpositions and achieve high-fidelity reconstruction using a ResNet-based decoding network. By incorporating transfer learning, the model demonstrates strong generalization across different wavelengths and spectral linewidths, attaining a reconstruction accuracy of up to 99%. Furthermore, we develop an attention-enhanced DoubleU-Net to reconstruct complex images with rich edge structures. Experimental results verify that OAM filtering substantially enhances edge fidelity, improving the reconstruction accuracy by approximately 4% over the non-OAM case, reaching a final accuracy of 95%. These findings open avenues toward high-dimensional optical information processing, multimode fiber communications, and advanced computational imaging.

Source: Optics Express

DOI: 10.1364/oe.582669

Authors: Jiaqi Wang, Hu Zhang, Shengxi Zeng, He Wen, Jun Wang, Xiaoguang Zhang

Institution: Beijing University of Posts and Telecommunications

Language: English

Topic: Orbital Angular Momentum in Optics

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,270

Funders: National Natural Science Foundation of China, State Key Laboratory of Information Photonics and Optical Communications