Skip to main content
SpringerLink
Log in

Comparison of traditional and adaptive multi-scale products thresholding for enhancing the radiographs of welded object

  • Regular Article
  • Published:
The European Physical Journal Plus Aims and scope Submit manuscript

Abstract

The quality of welds is very important in some critical industries such as aeronautic manufacturing, pipelines, and civil construction. One of the most important weld inspections is industrial radiography testing. The quality of radiographic images may suffer from some level of blurriness due to poor signal-to-noise ratio, scattered X-rays, and other phenomena. Enhancing image contrast and improving defect detection may be achieved by digital image processing methods. In this study, outcomes from two thresholding algorithms are analyzed and compared. Here, to enhance the radiographic images, the dynamic wavelet transforms have been used long with two different types of thresholds, i.e., the traditional thresholding (including hard thresholding method) and the modified adaptive multi-scale products thresholding (MAMPT). The qualitative operator perception results show that two thresholding methods improved images contrasts and enhanced details visualization. Better results have been achieved by MAMPT especially around the defects regions of image. These results have been obtained by evaluation of the outputs from different algorithms. In addition, MAMPT is about 1.5 orders of magnitude quicker than the hard thresholding method making it more suitable for online weld inspection systems.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. C. Ajmi, J. Zapata, J.J. Martínez-Álvarez, G. Doménech, R. Ruiz, Using deep learning for defect classification on a small weld X-ray image dataset. J. Nondestruct. Eval. (2020). https://doi.org/10.1007/s10921-020-00719-9

    Article  Google Scholar 

  2. P. Sassi, P. Tripicchio, C.A. Avizzano, A smart monitoring system for automatic welding defect detection. IEEE Trans. Ind. (2019). https://doi.org/10.1109/TIE.2019.2896165

    Article  Google Scholar 

  3. E. Yahaghi, M.E. Hosseini-Ashrafi, Enhanced defect detection in radiography images of welded objects. Nondestruct. Test. Eval. 34(1), 13–22 (2019). https://doi.org/10.1080/10589759.2018.1544251

    Article  ADS  Google Scholar 

  4. U. Ewert, U. Zscherpel, M. Horkey, J. Kennedy, M. Hutchinson, A new computer based concept for digital radiographic reference images. J. Nondestruct. Test. 7(12), 1–13 (2002)

    Google Scholar 

  5. W. Hou, Y. Wei, J. Guo, Y. Jin, C. Zhu, Automatic detection of welding defects using deep neural network. J. Phys. 933, 012006 (2018). https://doi.org/10.1088/1742-6596/933/1/012006

    Article  Google Scholar 

  6. N. Boaretto, T.M. Centeno, Automated detection of welding defects in pipelines from radiographic images DWDI. NDT E Int. 86, 7–13 (2017). https://doi.org/10.1016/j.ndteint.2016.11.003

    Article  Google Scholar 

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

    Article  ADS  MathSciNet  Google Scholar 

  8. C. Stolojescu-Crişan, Ş Holban, A comparison of X-ray image segmentation techniques. Adv. Electr. Comput. Eng. 13(3), 85–92 (2013)

    Article  Google Scholar 

  9. A. Macovski, Noise in MRI. Magn. Reson. Med. 36, 494–497 (1996)

    Article  Google Scholar 

  10. S. Jangjit, M. Ketcham, A new wavelet denoising method for noise threshold. Eng. J. 21(7), 141–155 (2017). https://doi.org/10.4186/ej.2017.21.7.141

    Article  Google Scholar 

  11. D. Donoho, I. Johnstone, Ideal spatial adaption via wavelet shrinkage. Biometrika 81, 425–455 (1994)

    Article  MathSciNet  Google Scholar 

  12. Q. Pan, L. Zhang, G. Dai, H. Zhang, Two denoising methods by wavelet transform. IEEE Trans. Signal Process. 47(12), 3401–3406 (1999)

    Article  ADS  Google Scholar 

  13. M.S. Crouse, R.D. Nowak, R.G. Baraniuk, Wavelet-based signal processing using hidden Markov models. IEEE Trans. Signal Process. 46(4), 886–902 (1998)

    Article  ADS  MathSciNet  Google Scholar 

  14. S. Mallat, S. Zhong, Characterization of signals from multiscale edges. IEEE Trans. Pattern Anal. Mach. Intell. 14(7), 710–732 (1992)

    Article  Google Scholar 

  15. M. Biswas, H. Om, A new soft-thresholding image denoising method. Proc. Technol. 6, 10–15 (2012). https://doi.org/10.1016/j.protcy.2012.10.002

    Article  Google Scholar 

  16. P. Bao, L. Zhang, Noise reduction for magnetic resonance images via adaptive multiscale products thresholding. IEEE Trans. Med. Imaging 22(9), 1089–1099 (2003)

    Article  Google Scholar 

  17. A. Aggarwal, A. Garg, Medical image enhancement using adaptive multiscale product thresholding, in International A. Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) (IEEE, 2014). https://doi.org/10.1109/ICICICT.2014.6781362

  18. R. Sethunadh, T. Thomas, SAR image despeckling using adaptive multiscale products thresholding in directionlet domain. Electron. Lett. 49(18), 1183–1184 (2013). https://doi.org/10.1049/el.2013.2186

    Article  ADS  Google Scholar 

  19. M. Vijay, Adaptive spatial and wavelet multiscale products thresholding method for medical image denoising, in 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET) (2012), pp. 1109–1114

  20. D. Mery, V. Riffo, U. Zscherpel, G. Mondragón, I. Lillo, I. Zuccar, H. Lobel, M. Carrasco, GDXray: the database of X-ray images for non-destructive testing. J. Nondestruct. Eval. 34(4), 42 (2015)

    Article  Google Scholar 

  21. D. Baleanu, H. Aydin, Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology (InTech, London, 2012)

    Book  Google Scholar 

  22. A. Anshita, A. Garg, Medical image enhancement using adaptive multiscale product thresholding, in 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) (IEEE, 2014), pp. 683–687

  23. A. Rosenfeld, M. Thurston, Edge and curve detection for visual scene analysis. IEEE Trans. Comput. 100(5), 562–569 (1971)

    Article  Google Scholar 

  24. B.M. Sadler, A. Swami, Analysis of multiscale products for step detection and estimation. IEEE Trans. Inf. Theory 45(3), 1043–1051 (1999)

    Article  MathSciNet  Google Scholar 

  25. J. Canny, A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(2), 679–698 (1986)

    Article  Google Scholar 

  26. Y. Du, M. Tong, L. Zhou, H. Dong, Edge detection based on Retinex theory and wavelet multiscale product for mine images. Appl. Opt. 55(34), 9625–9637 (2016)

    Article  ADS  Google Scholar 

  27. P. Kovesi, Phase preserving denoising of images. Signal 4(1), 212–217 (1999)

    Google Scholar 

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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Physics, Imam Khomeini International University, Qazvin, Iran

    Effat Yahaghi

  2. Department of Mathematics, Faculty of Sciences, Bu-Ali Sina University, Hamedan, Iran

    Mahdi Mirzapour

  3. Nuclear Science and Technology Research Institute (NSTRI), Tehran, Iran

    Amir Movafeghi

Authors
  1. Effat Yahaghi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mahdi Mirzapour
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amir Movafeghi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahdi Mirzapour.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yahaghi, E., Mirzapour, M. & Movafeghi, A. Comparison of traditional and adaptive multi-scale products thresholding for enhancing the radiographs of welded object. Eur. Phys. J. Plus 136, 744 (2021). https://doi.org/10.1140/epjp/s13360-021-01733-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1140/epjp/s13360-021-01733-0

Search

Navigation