  This paper proposes crack segmentation augmented by super resolution (SR)
with deep neural networks. In the proposed method, a SR network is jointly
trained with a binary segmentation network in an end-to-end manner. This joint
learning allows the SR network to be optimized for improving segmentation
results. For realistic scenarios, the SR network is extended from non-blind to
blind for processing a low-resolution image degraded by unknown blurs. The
joint network is improved by our proposed two extra paths that further
encourage the mutual optimization between SR and segmentation. Comparative
experiments with State of The Art (SoTA) segmentation methods demonstrate the
superiority of our joint learning, and various ablation studies prove the
effects of our contributions.
