The life of an aircraft depends on the early detection and removal of corrosion in its structure. The importance of detecting corrosion cannot be understated, because corrosion can cause other kinds of damage, such as cracks. Radiography is an important method for the detection of hidden defects in aircraft structure. To maximize information extraction from the radiographic images, the noise of the system should be minimized, or the contrast of the defective region should be maximized by different methods. The development of effective image processing methods, within both the spatial and frequency domains, is important to the research of industrial radiographic testing. In this study, the geometric locally adaptive sharpening method was used to improve hidden structure visualization of details and defects from aircraft part radiographs. The method relies on sharpening by using the steering kernel regression method. Here, the enhancing contrast and sharpening algorithm are effectively mixed together. The proposed algorithm was successfully applied to radiographic images of aircraft parts. An improvement of the structure detail visualization and defect region detection was achieved by sharpening the edges and preserving fine detail imaging information. Experts’ reviews showed that defect regions from the geometric locally adaptive sharpening reconstructed images were better visualized than the original images. Also, the resulting evaluation of the output images shows that the edges are sharpened by the proposed method and that the background of the image decreases to zero.
J. Schijve, Int. J. Fatigue 31 (6), 998–1011 (2009). DOI: 10.1016/j.ijfatigue.2008.05.016.
A. Scott, and T. Hepher, Boeing reports wing cracks on 787 dreamliners in production. Reuters News Agency; Mar. 8, 2014.
A. Barter, L. Molent, and R. J. H. Wanhill, Int. J. Fatigue 41, 11–22 (2012). DOI: 10.1016/j.ijfatigue.2011.10.017.
B. Ghose et al., Proceedings of the national seminar & exhibition on non-destructive evaluation, nde, chennai, india, 8–10 Dec. 2011.
B. Li, Y. Shen, and W. Hu, Mater. Des. 32 (4), 2073–2084 (2011). DOI: 10.1016/j.matdes.2010.11.054.
R. Hanke, T. Fuchs, and N. Uhlmann, Nucl. Instrum. Methods Phys. Res. A 591 (1), 14–18 (2008). DOI: 10.1016/j.nima.2008.03.016.
A. S. S. Silva et al., Mater. Eval. 72(3), 392–397 (2014).
A. Polesel, G. Ramponi, and V. J. Mathews, IEEE Trans. Image Process. 9 (3), 505–510 (2000). DOI: 10.1109/83.826787.
B. Zhang and J. P. Allebach, IEEE Trans. Image Process. 17 (5), 664–678 (2008). DOI: 10.1109/TIP.2008.919949.
G. Cao et al., IEEE Signal Process Lett. 18 (10), 603–606 (2011). DOI: 10.1109/LSP.2011.2164791.
X. Zhu and P. Milanfar, IEEE Workshop Appl. Comput. Vision WACV 103–109 (2011). DOI: 10.1109/WACV.2011.5711490.
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).
ISO 14096-2, Non-Destructive Testing - Qualification of Radiographic Film Digitisation Systems - Part 2: Minimum Requirement. (International Organization for Standardization, Switzerland, 2005).
ISO 17636-2, Non-Destructive Testing of Welds - Radiographic Testing - Part 2: X- and Gamma-Ray Techniques with Digital Detectors. (International Organization for Standardization, Switzerland, 2013).
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