In the process of research, development, production, service, and maintenance of silicon photovoltaic (Si-PV) cells and the requirements for detection technology are becoming more and more important. This paper aims to investigate electromagnetic induction (EMI) and image fusion to improve the detection effect of electrothermography (ET) and electroluminescence (EL) of multidefects in Si-PV cells. First, the principles of ET, EL, and other physical processes including EMI, thermal radiation, and luminescence radiation are analyzed in this paper. ET and EL techniques after EMI improvement are used to detect different defects including scratch, broken gridline, surface impurity, hidden crack, and so on. The qualitative results show that EMI can greatly improve the defect detection ability of ET and EL. Then, an image-fusion rule based on L1 norm is proposed to fuse the sparse vector of the ET and EL images. The integration and complementarity of the two wavelength detection data are achieved. Finally, the image-fusion results of sparse representation (SR) algorithm is compared with discrete wavelet transform, curvelet transform, dual-tree complex wavelet transforms, and nonsubsampled contourlet transform. Five objective evaluation indexes including root mean square error, peak signal-to-noise ratio, correlation coefficient, mutual information, and structural similarity index are used to evaluate the fusion results. Overall evaluation results show that the SR algorithm is superior to the other algorithms.

中文翻译：

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硅光伏电池电热成像和电致发光的电磁感应加热和图像融合

在硅光伏（Si-PV）电池的研究，开发，生产，服务和维护过程中，对检测技术的要求变得越来越重要。本文旨在研究电磁感应（EMI）和图像融合，以提高Si-PV电池中多缺陷的电热成像（ET）和电致发光（EL）的检测效果。首先，本文分析了ET，EL和其他物理过程的原理，包括EMI，热辐射和发光辐射。EMI改善后的ET和EL技术用于检测不同的缺陷，包括刮擦，断裂的网格线，表面杂质，隐藏的裂纹等。定性结果表明，EMI可以大大提高ET和EL的缺陷检测能力。然后，提出了一种基于L1范数的图像融合规则，以融合ET和EL图像的稀疏矢量。实现了两个波长检测数据的积分和互补。最后，将稀疏表示（SR）算法的图像融合结果与离散小波变换，curvelet变换，双树复小波变换和非下采样contourlet变换进行了比较。使用五个客观评估指标，包括均方根误差，峰值信噪比，相关系数，互信息和结构相似性指标，来评估融合结果。总体评估结果表明，SR算法优于其他算法。最后，将稀疏表示（SR）算法的图像融合结果与离散小波变换，curvelet变换，双树复小波变换和非下采样contourlet变换进行了比较。五个均方根误差，峰值信噪比，相关系数，互信息和结构相似性指标的客观评价指标用于评价融合结果。总体评估结果表明，SR算法优于其他算法。最后，将稀疏表示（SR）算法的图像融合结果与离散小波变换，curvelet变换，双树复小波变换和非下采样contourlet变换进行了比较。使用五个客观评价指标，包括均方根误差，峰信噪比，相关系数，互信息和结构相似性指标，对融合结果进行评价。总体评估结果表明，SR算法优于其他算法。相互信息和结构相似性指标用于评估融合结果。总体评估结果表明，SR算法优于其他算法。相互信息和结构相似性指标用于评估融合结果。总体评估结果表明，SR算法优于其他算法。