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This paper proposes crack segmentation augmented by super resolution (SR)with deep neural networks. In the proposed method, a SR network is jointlytrained with a binary segmentation network in an end-to-end manner. This jointlearning allows the SR network to be optimized for improving segmentationresults. For realistic scenarios, the SR network is extended from non-blind toblind for processing a low-resolution image degraded by unknown blurs. Thejoint network is improved by our proposed two extra paths that furtherencourage the mutual optimization between SR and segmentation. Comparativeexperiments with SoTA segmentation methods demonstrate the superiority of ourjoint learning, and various ablation studies prove the effects of ourcontributions.

Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images