Imaging through multimode fibres with physical prior

TL;DR

This paper introduces a physics-assisted, unsupervised learning approach for imaging through perturbed multimode fibres, reducing training data needs and improving generalization without relying on paired training data.

Contribution

It presents a novel physics-assisted, unsupervised fibre imaging scheme that simplifies the mapping process and enhances robustness in perturbed multimode fibres.

Findings

Requires fewer speckle patterns for reconstruction

Does not need paired training data

Improves generalization in perturbed conditions

Abstract

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.