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 SoTA segmentation methods demonstrate the superiority of our joint learning, and various ablation studies prove the effects of our contributions.

翻译：本文提出了一种通过深度神经网络实现超分辨率增强的裂缝分割方法。该方法中，超分辨率网络与二值分割网络以端到端方式联合训练。这种联合学习机制使得超分辨率网络能够针对分割结果的优化进行专门调整。针对真实场景，超分辨率网络从非盲模式扩展至盲模式，以处理由未知模糊退化的低分辨率图像。我们提出的两条额外路径进一步促进了超分辨率与分割之间的相互优化，从而改进了联合网络。与当前最先进分割方法的对比实验证明了联合学习的优越性，而各种消融研究则验证了我们贡献的有效性。