Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Interlaced bilateral filtering and wavelet thresholding for flaw detection in the radiography of weldments

  • 10 Accesses


The recognition of damages and defects is very important in welded joints radiographic images. The experts’ evaluations of the weld images are dependent on both image quality and interpreter’s experience. This evaluation will become difficult if the radiography images have low quality and have been corrupted with noise and other quality decreasing factors. Therefore, some image processing methods are required to improve the radiography images’ quality. Here, an interlaced multistage bilateral filtering and wavelet thresholding have been implemented to radiography images of welded objects for better detection of weld flaws, which can effectively obviate the defect regions in real noisy radiography images. The line profile method has been implemented for the evaluation of the results, i.e., the quality of the radiographs before and after applying the image processing algorithm. The line profiles were compared in the image quality indicator region. The comparison of the line profiles shows that the quality of the reconstructed radiographs, which is measured here by the contrast to background level, enhances almost by a factor of two for the subtracted images in comparison with the original radiographs.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9


  1. 1.

    E. Deprins, Computed radiography in ndt applications. Insight Nondestruct. Test. Cond. Monit. 46(10), 590–593 (2004)

  2. 2.

    A. Movafeghi, Using empirical mode decomposition and a fuzzy algorithm for the analysis of weld defect images. Insight Nondestruct. Test. Cond. Monit. 57(1), 35–39 (2015)

  3. 3.

    R.H. Bossi, F.A. Iddings, G.C. Wheeler (eds.), Radiographic Testing. Nondestructive testing handbook, 3rd edn. (American Society for Nondestructive Testing, Columbus, 2002)

  4. 4.

    V.R. Rathod, R. Anand, A comparative study of different segmentation techniques for detection of flaws in nde weld images. J. Nondestruct. Eval. 31(1), 1–16 (2012)

  5. 5.

    E. Yahaghi, The detection of weld defect images using shape-from-shading and wavelet denoising methods. Insight Nondestruct. Test. Cond. Monit. 56(6), 308–311 (2014)

  6. 6.

    D. Mery, M.A. Berti, Automatic detection of welding defects using texture features. Insight Nondestruct. Test. Cond. Monit. 45(10), 676–681 (2003)

  7. 7.

    E. Yahaghi, M. Mirzapour, A. Movafeghi, P. Mohammadi Matin, B. Rokrok, Fista algorithm for radiography images enhancement with background blurring removal. Res. Nondestruct. Eval. 30(2), 80–88 (2019)

  8. 8.

    Y. Wang, Y. Sun, P. Lv, H. Wang, Detection of line weld defects based on multiple thresholds and support vector machine. Ndt E Int. 41(7), 517–524 (2008)

  9. 9.

    S.G. Chang, B. Yu, M. Vetterli, Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process. 9(9), 1532–1546 (2000)

  10. 10.

    A. Pizurica, W. Philips, Estimating the probability of the presence of a signal of interest in multiresolution single-and multiband image denoising. IEEE Trans. Image Process. 15(3), 654–665 (2006)

  11. 11.

    S. Paris, F. Durand, A fast approximation of the bilateral filter using a signal processing approach, in European Conference on Computer Vision (Springer, 2006), pp. 568–580

  12. 12.

    K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, Image denoising with block-matching and 3d filtering, in Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, vol. 6064 (International Society for Optics and Photonics, 2006), p. 606414

  13. 13.

    M. Zhang, B.K. Gunturk, Multiresolution bilateral filtering for image denoising. IEEE Trans. Image Process. 17(12), 2324–2333 (2008)

  14. 14.

    C. Liu, W.T. Freeman, R. Szeliski, S.B. Kang, Noise estimation from a single image, in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 1 (IEEE, 2006), pp. 901–908

  15. 15.

    C. Tomasi, R. Manduchi, Bilateral filtering for gray and color images, in Iccv, vol. 98 (1998), p. 2

  16. 16.

    V. Aurich, J. Weule, Non-linear gaussian filters performing edge preserving diffusion, in Mustererkennung 1995 (Springer, 1995), pp. 538–545

  17. 17.

    S.M. Smith, J.M. Brady, Susan—a new approach to low level image processing. Int. J. Comput. Vis. 23(1), 45–78 (1997)

  18. 18.

    D.N. Thanh, V.S. Prasath, L.M. Hieu, A review on CT and x-ray images denoising methods. Informatica 43, 9 (2019)

  19. 19.

    B. Zhang, J.M. Fadili, J.-L. Starck, Wavelets, ridgelets, and curvelets for poisson noise removal. IEEE Trans. Image Process. 17(7), 1093–1108 (2008)

  20. 20.

    E. Yahaghi, A. Movafeghi, Contrast enhancement of industrial radiography images by gabor filtering with automatic noise thresholding. Russ. J. Nondestruct. Test. 55(1), 73–79 (2019)

  21. 21.

    ISO 14096-2, Non-destructive testing—qualification of radiographic film digitisation systems—Part II: Minimum requirement. International Organization for Standardization, Switzerland (2005)

  22. 22.

    ISO 14096-1, Non-destructive testing—qualification of radiographic film digitisation systems—Part I: Definitions, qualitative measurements of image quality parameters, standard reference film and qualitative control. International Organization for Standardization, Switzerland (2005)

  23. 23.

    Microtek, Operation manual of Scanmaker-1000 scanner, Microtek Co. (2005)

Download references

Author information

Correspondence to Mahdi Mirzapour.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yahaghi, E., Mirzapour, M., Movafeghi, A. et al. Interlaced bilateral filtering and wavelet thresholding for flaw detection in the radiography of weldments. Eur. Phys. J. Plus 135, 42 (2020). https://doi.org/10.1140/epjp/s13360-020-00119-y

Download citation