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

• URL: http://arxiv.org/abs/2302.12491v3
• Date: Sun, 25 Feb 2024 17:25:16 GMT
• Title: Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images
• Authors: Yuki Kondo and Norimichi Ukita
• Abstract summary: This paper proposes crack segmentation augmented by super resolution (SR) with deep neural networks. A SR network is jointly trained with a binary segmentation network in an end-to-end manner. For realistic scenarios, the SR network is extended from non-blind to blind for processing a low-resolution image degraded by unknown blurs.
• Score: 16.497489431525565
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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