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.

通过深度学习的扰动多模光纤成像已经得到广泛研究，然而现有方法主要使用不同配置下的目标 - 散斑对，而没有经过训练的网络很难重建目标。本文提出了一种基于物理辅助的无监督学习光纤成像方案，物理先验的作用是简化散斑图案与目标图像之间的映射关系，从而减少计算复杂性。无监督网络根据物理先验提供的优化方向学习目标特征，因此在线学习的重建过程只需要少量的散斑图案和不成对的目标。该方案还增加了在扰动多模光纤中的基于学习的方法的泛化能力，具有扩展多模光纤成像应用的潜力。