基于经验小波的太阳能电池缺陷图像融合
Solar cell defect images fusion based on empirical wavelet

为解决太阳能电池的弱缺陷检测问题,提出一种基于二维张量经验小波的多光谱图像融合算法。使用一组特定波长的光源采集太阳能电池片图像信息,对图像进行顶帽变换抑制背景噪声;使用经验小波变换对预处理图像进行分解,分别对获得的高低频子带图像采用基于极大值的显著性融合规则进行融合,将融合后的高低频子带图像进行小波反变换获得最终的融合图像。在相同的采集条件下获取五类色差电池片图像,进行算法测试试验,并从图像视觉效果和客观评价指标两方面与其他算法分析比较。试验结果表明,此算法不仅具有良好的适应性,而且在保持光谱信息和抑制噪声等方面均取得良好的效果。
To solve the problem of the weak defect detection of solar cells, a multispectral image fusion algorithm based on 2D tensor empirical wavelet transform was proposed. The image information of solar cells was collected by using a set of specific wavelengths lights, and the noise was suppressed by using top-hat transformation. The preprocessed images were decomposed using empirical wavelet transform, and the obtained subband images of high and low frequency were fused using the saliency rules based on maximum value. The fused subband images of high and low frequency were transformed into the final image through inverse empirical wavelet transform. Cell images of five types of chromatic aberrations were acquired under the same acquisition conditions for testing algorithm, and were compared with other algorithms from two aspects of image visual effect and objective evaluation indexes. The experimental results showed that the proposed algorithm had good adaptability and good performance in the aspects of maintaining spectral information and suppressing noise.