Self-supervised dynamic learning for long-term high-fidelity image transmission through unstabilized diffusive media

Ziwei Li (), Wei Zhou, Zhanhong Zhou, Shuqi Zhang, Jianyang Shi, Chao Shen, Junwen Zhang, Nan Chi () and Qionghai Dai ()
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Ziwei Li: Fudan University
Wei Zhou: Fudan University
Zhanhong Zhou: Fudan University
Shuqi Zhang: Fudan University
Jianyang Shi: Fudan University
Chao Shen: Fudan University
Junwen Zhang: Fudan University
Nan Chi: Fudan University
Qionghai Dai: Fudan University

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-45745-7

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