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 WeiEmail author


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.


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


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


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).


  1. 1.
    Schreiber D, Cambrini L, Biber J, Sardy B (2009) Online visual quality inspection for weld seams [J]. Int J Adv Manuf Technol 42(5–6):497–504CrossRefGoogle Scholar
  2. 2.
    Zhang Z Jr, K A, Chen S, Huang Y (2015) Xu Y. Online defect detection of Al alloy in arc welding based on feature extraction of arc spectroscopy signal [J]. Int J Adv Manuf Technol 79(9–12):2067–2077CrossRefGoogle Scholar
  3. 3.
    Rodil SS, Gómez RA, Bernárdez JM, Rodriguez F, Miguel LJ, Perán JR (2010) Laser welding defects detection in automotive industry based on radiation and spectroscopical measurements [J]. Int J Adv Manuf Technol 49(1–4):133–145CrossRefGoogle Scholar
  4. 4.
    Rosado LS, Santos TG, Piedade M, Ramos PM, Vilaca P (2010) Advanced technique for non-destructive testing of friction stir welding of metals [J]. Measurement 43(8):1021–1030CrossRefGoogle Scholar
  5. 5.
    Kumar GS, Ananthan SS (2012) Vision inspection system for the identification and classification of defects in MIG welding joints [J]. Int J Adv Manuf Technol 61(9–12):923–933CrossRefGoogle Scholar
  6. 6.
    Li G, Li M, Wang X, Feng L, Zhang C (2013) Research on trend of defect in infrared non-destructive testing[J]. Acta Opt Sin F12:17–21Google Scholar
  7. 7.
    Sun Y, Bai P, Sun HY, Zhou P (2005) Real-time automatic detection of weld defects in steel pipe [J]. NDT&E International 38(7):522–528CrossRefGoogle Scholar
  8. 8.
    Ren DH, You Z, Sun CK, Ye SH (2001) Automatic inspection techniques by real-time radiography for weld defects [J]. Journal of Tsinghua University 41(2):25–29Google Scholar
  9. 9.
    Zhou ZG, Zhao S, An ZG (2004) Defect extraction of X-ray images based on subarea and self-adaptive median filtering [J]. Acta Aeronaut Astronaut Sin 25(4):420–424Google Scholar
  10. 10.
    Lashkia V (2001) Defect detection in X-ray images using fuzzy reasoning [J]. Image Vis Comput 19(5):261–269CrossRefGoogle Scholar
  11. 11.
    Zhang YX, Xie HL, Du GH, Chen RC, Xiao TQ (2014) Influence of scintillator’s thickness on imaging quality of lens-coupled hard X-ray imaging detector [J]. Nuclear Techniques 37(7):70102–070102Google Scholar
  12. 12.
    Zhou ZG, Teng SH, Jiang W, Li HP (2002) Research on defect detection and evaluation in welds with X-rays [J]. Trans China Weld Inst 23(3):85–88Google Scholar
  13. 13.
    Gang T, Wang DH (2001) Defect extraction and segmentation automatically in X-ray inspection images [J]. Welding & Joining 5:6–9 43 Google Scholar
  14. 14.
    Gao HX, Wu LX, Xu H, Kang H, Hu YM (2014) Denoising method of micro-focus X-ray images corrupted with mixed multiplicative and additive noises [J]. Opt Precis Eng 22(11):3100–3113CrossRefGoogle Scholar
  15. 15.
    Tang Y, Zhang X, Li X, Guan X (2009) Application of a new image segmentation method to detection of defects in castings [J]. Int J Adv Manuf Technol 43(5–6):431–439CrossRefGoogle Scholar
  16. 16.
    Liang P (2012) Research on automatic extraction and recognition technology of defect of X - ray weld seam image [D]. Nanjing University of Aeronautics and Astronautics, NanjingGoogle Scholar
  17. 17.
    Mukhopadhyay S, Mandal JK (2014) A fuzzy switching median filter of impulses in digital imagery (FSMF) [J]. Circuits Systems & Signal Processing 33(7):2193–2216CrossRefGoogle Scholar
  18. 18.
    Varghese J, Tairan N, Subash S (2015) Adaptive switching non-local filter for the restoration of salt and pepper impulse-corrupted digital images [J]. Arab J Sci Eng 40(11):3233–3246CrossRefGoogle Scholar
  19. 19.
    Nasri M, Saryazdi S, Nezamabadi-pour H (2013) A switching non-local means filter for removal of high density salt and pepper noise [J]. Scientia Iranica 20(3):760–764Google Scholar
  20. 20.
    Zhang R (2007) Impulse noise filter for color image [D]. Tianjin University, TianjinGoogle Scholar
  21. 21.
    Xie JC, Zhang DL, Xu WL (2002) Overview on wavelet image denoising [J]. Journal of Image & Graphics 7(3):3–11Google Scholar
  22. 22.
    Zhang L (2006) Study on multi wavelet denoising method [D]. Jiangxi University of Finance and Economics, NanchangGoogle Scholar
  23. 23.
    Long Y, Liu MK, Yin ZK, Wang JY (2008) Adaptive image enhancement algorithm based on Bandelet transform domain [J]. Journal of Computer Applications 28(5):1221–1224CrossRefGoogle Scholar
  24. 24.
    Peng Z, Zhao BZ (2011) Novel scheme for infrared image enhancement based on Contourlet transform [J]. International Conference on Electronic & Mechanical Engineering & Information Technology 41(6):3134–3137Google Scholar
  25. 25.
    Yang B, Jia ZH, Qin XZ, Yang J, Hu R (2015) Remote sensing image enhancement based on Shearlet transform [J]. Journal of Computer Applications 24(11):2249–2253Google Scholar
  26. 26.
    Chen G (2007) Research on the digital image processing and defect detection method of X-ray in the weld [D]. Lanzhou University of Technology, GansuGoogle Scholar
  27. 27.
    Huang XT (1985) Two dimensional digital signal processing [M]. Science Press, BeijingGoogle Scholar
  28. 28.
    Burch SF (1987) Digital enhancement of video images for NDT [J]. NDT Int 20(1):51–56Google Scholar
  29. 29.
    Wang XK, Li F (2010) Improved adaptive median filter [J]. Computer Engineering and Applications 46(3):175–176 218Google Scholar
  30. 30.
    Xu J, Chen SH (2015) Application of wavelet analysis on image denoising [J]. Electronic Design Engineering 1:185–187Google Scholar
  31. 31.
    Liu XW, Yin GF, Li SY (2001) Adaptive fuzzy enhancement algorithm and its application on engineering scanning drawing [J]. Journal of. Mach Des 18(1):4–6Google Scholar

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
    Email author
  1. 1.Nanjing University of Aeronautics and AstronauticsCollege of Material Science and TechnologyNanjingChina
  2. 2.Shanghai Aircraft Manufacturing Co., LtdInstitute of Aeronautical Manufacturing TechnologyShanghaiChina

Personalised recommendations