Imaging through multimode fibres with physical prior
This work addresses a challenge in multimode fiber imaging for applications like medical endoscopy or industrial inspection, offering an incremental improvement over existing deep learning methods.
The paper tackles the problem of reconstructing images through perturbed multimode fibers without requiring trained networks, by proposing a physics-assisted unsupervised learning scheme that reduces computational complexity and improves generalization, achieving reconstruction with only a few speckle patterns and unpaired targets.
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.