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.

翻译：基于深度学习的扰动多模光纤成像已被广泛研究。然而，现有方法主要使用不同配置下的目标与散斑对，在没有训练网络的情况下重建目标具有挑战性。本文提出一种物理辅助的无监督学习光纤成像方案。物理先验的作用在于简化散斑图与目标图像之间的映射关系，从而降低计算复杂度。无监督网络依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仅需少量散斑图与未配对的非成对目标。该方案还增强了基于学习的方法在扰动多模光纤中的泛化能力，具有拓展多模光纤成像应用范围的潜力。