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

To cope with in the world, it is difficult to always manually inspect all of these problems, this paper proposes a unified framework them. Instead of the manual inspection, automatic inspection consisting of the following novel contributions (Table 1): is one of the prospective solutions for efficiently diagnosing 1. Crack Segmentation with Blind Super-Resolution these constructions. While such inspection can be achieved (CSBSR): As with Crack Segmentation with Super by several types of sensors such as the Falling Weight Deflectometer, Resolution (CSSR) proposed in our earlier conference the Pavement Density Profiler, and the Ground paper [60], CSBSR proposed in this paper connects "a Penetrating Radar, this paper focuses on crack segmentation network for Super Resolution (SR) accepting an input on images captured by generic cameras for visual inspection. LR image" in series to "a segmentation network" for Crack segmentation [31] is defined to be binary semantic end-to-end joint learning. We extend CSSR to CSBSR segmentation in the field of computer vision. While the with blind SR to handle realistically-blurred images.