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- Title
Self-supervised dynamic learning for long-term high-fidelity image transmission through unstabilized diffusive media.
- Authors
Li, Ziwei; Zhou, Wei; Zhou, Zhanhong; Zhang, Shuqi; Shi, Jianyang; Shen, Chao; Zhang, Junwen; Chi, Nan; Dai, Qionghai
- Abstract
Multimode fiber (MMF) which supports parallel transmission of spatially distributed information is a promising platform for remote imaging and capacity-enhanced optical communication. However, the variability of the scattering MMF channel poses a challenge for achieving long-term accurate transmission over long distances, of which static optical propagation modeling with calibrated transmission matrix or data-driven learning will inevitably degenerate. In this paper, we present a self-supervised dynamic learning approach that achieves long-term, high-fidelity transmission of arbitrary optical fields through unstabilized MMFs. Multiple networks carrying both long- and short-term memory of the propagation model variations are adaptively updated and ensembled to achieve robust image recovery. We demonstrate >99.9% accuracy in the transmission of 1024 spatial degree-of-freedom over 1 km length MMFs lasting over 1000 seconds. The long-term high-fidelity capability enables compressive encoded transfer of high-resolution video with orders of throughput enhancement, offering insights for artificial intelligence promoted diffusive spatial transmission in practical applications. This work introduces a cutting-edge technique to overcome dynamic scattering challenges in long-distance multimode fiber transmission, achieving >99.9% accuracy for 1024 modes over 1 km, hence promises applications in diverse scattering scenarios.
- Subjects
IMAGE transmission; LIGHT propagation; ARTIFICIAL intelligence; OPTICAL communications; SHORT-term memory; IMAGE stabilization; SUPERVISED learning
- Publication
Nature Communications, 2024, Vol 15, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-024-45745-7