In this work, we present a novel tool for reconstructing networks from corrupted images. The reconstructed network is the result of a minimization problem that has a misfit term with respect to the observed data, and a physics-based regularizing term coming from the theory of optimal transport. Through a range of numerical tests, we demonstrate that our suggested approach can effectively rebuild the primary features of damaged networks, even when artifacts are present.

翻译：本文提出了一种基于受损图像重建网络的新颖工具。重建网络通过最小化问题获得，该问题包含与观测数据不匹配的误差项以及源自最优传输理论的物理正则化项。通过一系列数值实验，我们证明所提出的方法能够有效重建受损网络的主要结构特征，即使在存在伪影的情况下也能实现准确复原。