Journal of Shandong University(Engineering Science) ›› 2018, Vol. 48 ›› Issue (5): 24-31.doi: 10.6040/j.issn.1672-3961.0.2018.249

• Machine Learning & Data Mining • Previous Articles     Next Articles

Solar cell defect images fusion based on empirical wavelet

Haiyong CHEN(),Li YU*(),Hui LIU,Jiabo YANG,Qidi HU   

  1. School of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, China
  • Received:2018-06-07 Online:2018-10-01 Published:2018-06-07
  • Contact: Li YU E-mail:haiyong.chen@hebut.edu.cn;1094141015@qq.com
  • Supported by:
    国家自然科学基金资助项目(61403119);河北省自然科学基金资助项目(F2018202078);河北省科技计划资助项目(17211804D);河北省青年拔尖人才资助项目(210003)

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Abstract:

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.

Key words: empirical wavelet, multispectral image fusion, saliency, Top-hat transformation, solar cell

CLC Number: 

  • TP391

Fig.1

The images of the solar cell in a variety of spectra"

Fig.2

The original images grayscale histogram"

Fig.3

The results of each wavelength image pretreatment"

Fig.4

The results of anti-color processing of each wavelength image"

Fig.5

β function image"

Fig.6

Solar cell scratch defect spectrum residual distribution curves"

Fig.7

Defect image and its saliency analysis results"

Fig.8

Overall algorithm architecture"

Table 1

Panel fusion index for different background colors"

电池片颜色融合前各项指标均值 融合后各项指标
信息熵 标准差 清晰度 信息熵 标准差 清晰度
深深蓝 3.39 10.60 1.98 4.08 10.30 2.24
深蓝 3.06 5.36 1.36 4.02 6.72 2.08
蓝 3.45 8.98 2.00 4.36 9.90 2.67
浅蓝 3.07 4.80 1.34 3.89 5.19 1.93
浅浅蓝 3.01 3.66 1.21 4.06 5.11 1.89

Fig.9

Multiple algorithms fusion images"

Table 2

The index of fusion images of different algorithms"

算法 信息熵 PSNR 清晰度 标准差
本研究算法 4.36 35.6 2.67 9.90
形态金字塔 4.18 33.6 2.51 9.75
梯度金字塔 3.73 29.3 2.53 8.56
离散小波 4.29 35.2 2.84 9.12
双树复小波 3.98 29.5 2.36 9.32
曲波变换 4.13 32.2 2.62 9.52
非下采样轮廓波变换 4.42 31.5 2.47 9.17
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