  Imaging through perturbed multimode fibres based on deep learning has been
widely researched. However, existing methods mainly use target-speckle pairs in
different configurations. It is challenging to reconstruct targets without
trained networks. In this paper, we propose a physics-assisted, unsupervised,
learning-based fibre imaging scheme. The role of the physical prior is to
simplify the mapping relationship between the speckle pattern and the target
image, thereby reducing the computational complexity. The unsupervised network
learns target features according to the optimized direction provided by the
physical prior. Therefore, the reconstruction process of the online learning
only requires a few speckle patterns and unpaired targets. The proposed scheme
also increases the generalization ability of the learning-based method in
perturbed multimode fibres. Our scheme has the potential to extend the
application of multimode fibre imaging.
