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MA Zengqiang, QIAN Rongwei, XU Dandan, DU Wei. Denoising of line structured light welded seams image based on adaptive top-hat transform[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2021, 42(2): 8-15. DOI: 10.12073/j.hjxb.20200519002
Citation: MA Zengqiang, QIAN Rongwei, XU Dandan, DU Wei. Denoising of line structured light welded seams image based on adaptive top-hat transform[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2021, 42(2): 8-15. DOI: 10.12073/j.hjxb.20200519002
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Denoising of line structured light welded seams image based on adaptive top-hat transform

  • 1.

    Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

  • 2.

    State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

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  • Received Date: May 18, 2020
  • Available Online: February 05, 2021
    Graphical Abstract
    • Abstract
  • Due to the scattering of the welding material, surface shape and laser line, the brightness distribution of the line structure is uneven and there are a lot of scattering noise around it, which affects the subsequent feature point extraction. In order to filter out the noise of the weld image of line structured light, a denoising model of weld image based on adaptive Top-Hat transformation is proposed. First, the noise image is transformed to a certain range of structural element size by Top-hat transform, and then an evaluation index was proposed, cross-correlation coefficient of image histogram (CCIH), and the optimal structural element size L is selected. Second, on the premise of the optimal structural element size L, the noise image is processed by the top-hat transform with a certain range of iterations times, a new model based on Otsu proposed SSIM model is proposed to select the optimal iterations time I from the evaluation index, Ratio of Structural Similarity Index to Average Brightness (RSB). Experimental results show that, compared with the method of Median Filter (MF), the method of Total Variation (TV), and the method combining Non-Subsampled Contourlet Transform with Total Variation (NSCT-TV), the proposed method has a great improvement in the aspects of subjective visual effect, entropy (EN), peak signal-to-noise ratio (PSNR) and mean-square error (MSE). Noise is suppressed more effectively and the line structured light area is preserved better in the image.
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    • References (24)
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