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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. [ABSTRACT FROM AUTHOR]

Hyperspectral Image (HSI) visualization, which aims at displaying as much material information of original images as possible on a trichromatic monitor with natural color, plays an important role in image interpretation and analysis. However, most of the HSI visualization methods only focus on presenting the detail information of a scene without providing natural colors and distinguishing land covers with similar colors. In order to address this problem, this article proposes a multichannel pulse-coupled neural network (MPCNN)-based HSI visualization method, which consists of the following steps. First, the MPCNN is proposed and explored to fuse the original HSI so as to obtain a fused band with rich spatial details. Then, a color mapping scheme is proposed to determine the weights of red, green, and blue (RGB) channels. Finally, the weighted RGB channels are stacked together for visualization. Experiments performed on four hyperspectral data sets demonstrate that the proposed method not only displays the HSI with nature colors but also improves the details in the image. The effectiveness of the proposed method is demonstrated in terms of both visual effect and objective indexes. [ABSTRACT FROM AUTHOR]