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

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.