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Research on X-ray image processing technology for laser welded joints of aluminum alloy

  • Xiaohong Zhan
  • Dan Zhang
  • Haisong Yu
  • Jie Chen
  • Hao Li
  • Yanhong Wei
ORIGINAL ARTICLE
  • 70 Downloads

Abstract

Weldment X-ray image processing has great significance on the subsequent segmentation and extraction of weld seam and defects. In the current study, X-ray image for laser welding of aluminum alloy was adopted as experimental object. A new image denoising algorithm was proposed, which was combined with weighted adaptive median filter and noise-reduction algorithm based on wavelet transform. An improved adaptive fuzzy enhancement algorithm was put forward, which was based on traditional Pal-King fuzzy enhancement algorithm. Different noise-reduction algorithms were performed to denoise the X-ray image. Comprehensive evaluation of noise-reduction effect was conducted by comparing the processing effect, 3D grayscale distribution, and image quality evaluation index after different denoising algorithms. Moreover, fuzzy entropy and fuzzy index were used to estimate the enhancement effect of traditional Pal-King fuzzy enhancement algorithm and improved adaptive fuzzy enhancement algorithm. The results revealed that the noise-reduction effect and the image quality obtained by proposed algorithm were better than separately using weighted adaptive median filter or noise-reduction algorithm based on wavelet transform. Furthermore, after processing with improved adaptive fuzzy enhancement algorithm, the image detail information was more prominent, and sense of hierarchy was stronger.

Keywords

X-ray NDT Image processing Noise-reduction algorithm Enhancement algorithm 

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Notes

Funding Information

This study is supported by the project from the National Natural Science Foundation of China (U1637103) and Shanghai Aerospace Science and Technology Innovation Foundation (SAST2017-061) and National Key Research and Development Plan “Intelligent Robot” key item (2017YFB1301600).

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Xiaohong Zhan
    • 1
  • Dan Zhang
    • 1
  • Haisong Yu
    • 1
  • Jie Chen
    • 1
    • 2
  • Hao Li
    • 2
  • Yanhong Wei
    • 1
  1. 1.Nanjing University of Aeronautics and AstronauticsCollege of Material Science and TechnologyNanjingChina
  2. 2.Shanghai Aircraft Manufacturing Co., LtdInstitute of Aeronautical Manufacturing TechnologyShanghaiChina

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