Visual inspection of solar modules is an important monitoring facility in photovoltaic power plants. Since a single measurement of fast CMOS sensors is limited in spatial resolution and often not sufficient to reliably detect small defects, we apply multiframe superresolution (MFSR) to a sequence of low-resolution measurements. In addition, the rectification and removal of lens distortion simplifies subsequent analysis. Therefore, we propose to fuse this preprocessing with standard MFSR algorithms. This is advantageous, because we omit a separate processing step, the motion estimation becomes more stable and the spacing of high-resolution pixels on the rectified module image becomes uniform w.r.t. the module plane, regardless of perspective distortion. We present a comprehensive user study showing that MFSR is beneficial for defect recognition by human experts and that the proposed method performs better than the state-of-the-art. Furthermore, we apply automated crack segmentation and show that the proposed method performs $3\times$ better than bicubic upsampling and $2\times$ better than the state-of-the-art for automated inspection.

中文翻译：

---

太阳能电池检测的联合超分辨率和校正

太阳能组件的目视检查是光伏电站中重要的监控设施。由于快速 CMOS 传感器的单次测量受到空间分辨率的限制，并且通常不足以可靠地检测小缺陷，因此我们将多帧超分辨率 (MFSR) 应用于一系列低分辨率测量。此外，镜头畸变的校正和去除简化了后续分析。因此，我们建议将此预处理与标准 MFSR 算法相融合。这是有利的，因为我们省略了单独的处理步骤，运动估计变得更加稳定，并且校正后的模块图像上高分辨率像素的间距在模块平面上变得均匀，而不管透视失真如何。我们提出了一项全面的用户研究，表明 MFSR 有利于人类专家的缺陷识别，并且所提出的方法比最先进的方法表现更好。此外，我们应用了自动裂纹分割并表明所提出的方法执行$3\times$ 优于双三次上采样和 $2\times$ 优于最先进的自动检测。