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Multimode fibres (MMF) are remarkable high-capacity information channelsowing to the large number of transmitting fibre modes, and have recentlyattracted significant renewed interest in applications such as opticalcommunication, imaging, and optical trapping. At the same time, the opticaltransmitting modes inside MMFs are highly sensitive to external perturbationsand environmental changes, resulting in MMF transmission channels being highlyvariable and random. This largely limits the practical application of MMFs andhinders the full exploitation of their information capacity. Despite greatresearch efforts made to overcome the high variability and randomness insideMMFs, any geometric change to the MMF leads to completely differenttransmission matrices, which unavoidably fails at the information recovery.Here, we show the successful binary image transmission using deep learningthrough a single MMF, which is stationary or subject to dynamic shapevariations. We found that a single convolutional neural network has excellentgeneralisation capability with various MMF transmission states. This deepneural network can be trained by multiple MMF transmission states to accuratelypredict unknown information at the other end of the MMF at any of these states,without knowing which state is present. Our results demonstrate that deeplearning is a promising solution to address the variability and randomnesschallenge of MMF based information channels. This deep-learning approach is thestarting point of developing future high-capacity MMF optical systems anddevices, and is applicable to optical systems concerning other diffusing media.

Deep learning the high variability and randomness inside multimode fibres