Article Article
The Performance of Three Total Variation Based Algorithms for Enhancing the Contrast of Industrial Radiography Images

Industrial radiography is considered as one of the most important nondestructive testing methods for different inspections. The radiography images often have a poor signal-to-noise ratio mainly because of the scattered X-rays. Image processing methods may be used to enhance the contrast of radiographs for better defect detection. In this study, outcomes from three total variations (TV) based methods were analyzed and compared. Implemented algorithms were ROF-TV, non-convex p-norm total variation (NCP-TV) and non-convex logarithm-based total variation (NCLog-TV). These TV-based methods have been implemented indirectly as high pass edge-enhancing filters. Based on qualitative operator perception results, the study has shown that the application of all three methods resulted in improved image contrast enabling enhanced image detail visualization. Subtle performance differences between the outputs from different algorithms were noted, however, especially around the edges of image features. Furthermore, it was found that all implemented algorithms have similarities in performance, generate approximately the same results and are suitable for weld inspection.

DOI: https://doi.org/10.1080/09349847.2020.1836293

References

S. Dong et al., Nat. Gas Indust. B 6 (4), 399–403 (2019). DOI: 10.1016/j.ngib.2019.01.016.

N. Boaretto and T. M. Centeno, Ndt & E Int. 86, 7–13 (2017). DOI: 10.1016/j.ndteint.2016.11.003.

D. Mery, Computer Vision for X-ray Testing (Springer International Publishing, Switzerland, 2015), Vol. 10, pp. 978–983.

C. Stolojescu-CriŞan and Ş. Holban, Adv. Electr. Comput. Eng. 1 3 (3).

L. Fan et al., Visual Comput. Indust. Biomed. Art 2 (1), 7 (2019).

M. Diwakar and M. Kumar, Biomed. Signal Process. Control 42, 73–88 (2018). DOI: 10.1016/j.bspc.2018.01.010.

L. I. Rudin, S. Osher, and E. Fatemi, Physica D 60 (1–4), 259–268 (1992). DOI: 10.1016/0167-2789(92)90242-F.

P. Ochs et al., Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 1759–1766.

E. J. Candes, M. B. Wakin, and S. P. Boyd, J. Fourier Anal. Appl. 14 (5–6), 877–905 (2008). DOI: 10.1007/s00041-008-9045-x.

X. Chen and W. Zhou, Comput. Optim. Appl. 59 (1–2), 47–61 (2014). DOI: 10.1007/s10589-013-9553-8.

R. Chartrand, 2007 IEEE International Conference on Image Processing, IEEE, 2007, Vol. 1, pp. I–293.

E. Yahaghi, Insight-Non-Destr. Test. Condition Monit. 58 (4), 201–205 (2016). DOI: 10.1784/insi.2016.58.4.201.

M. Lysaker and X.-C. Tai, Int. J. Comput. Vis. 66 (1), 5–18 (2006). DOI: 10.1007/s11263-005-3219-7.

S. Oh et al., J. Vis. Commun. Image Represent. 24 (3), 332–344 (2013). DOI: 10.1016/j.jvcir.2013.01.010.

D. Mery et al., J. Nondestruct. Eval. 34 (4), 42 (2015). DOI: 10.1007/s10921-015-0315-7.

 

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