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

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.

SUBMITTER: Li Z 

PROVIDER: S-EPMC10876540 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature