FISTA Algorithm for Radiography Images Enhancement with Background Blurring Removal

Testing and detecting potential defects in works of art is usually required to cause minimal or no damage; industrial Radiography Testing (RT) is often the method of choice provided the images are of the required high quality and yield high defect detection sensitivity. Various digital image processing methods can be employed to achieve improved image quality and information extraction as required. The level and nature of the noise in the RT images is usually unknown. In addition to the different noises, the image quality is degraded by the blurring effect. In this study, a modified form of the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) with quadratic regularization strategy was developed and applied to remove the blurring. The output of the application of the method to number RT images of five art objects was evaluated by industrial radiography and antiquities experts. It was confirmed that the applied method was efficient and improved image quality and defect detection.

References

[1] D. A. Scott, J. Ponday, and B. B. Considine, J. Paul Getty Mus. and the Getty Conservation Institute, Nov. 1991 (2007).

[2] D. Gavrilov, R. G. Maev, and D. P. Almond, Can. J. Phys. 92, 341–364 (2014). DOI: 10.1139/cjp-2013-0128.

[3] T. Malkogeorgou, J. Conserv. Mus. Stud. 10 (2), 1–7 (2013). DOI: 10.5334/jcms.1021203.

[4] V. A. Udod, Y. Van, and S. P. Osipov, Russ. J. Nondest. Test. 52(9), 492–503 (2016). DOI: 10.1134/S1061830916090072.

[5] T. Weissman et al., IEEE Trans. Inform. Theory 51, 5–28 (2005). DOI: 10.1109/TIT.2004.839518.

[6] A. C. Bovik, The Essential Guide to Image Processing (Elsevier, 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA, 2009).

[7] G. H. Golub and C. F. Van Loan, Matrix Computations (The John Hopkins University press, Baltimore, 2012), Vol. 3.

[8] T. Goldstein and S. Osher, SIAM J. Imaging Sci. 2(2), 323–343 (2009). DOI: 10.1137/080725891.

[9] A. Teboulle and P.-L. Lions, NumerischeMathematik 76(2), 167–188 (1997).

[10] A. Buades, B. Coll, and J.-M. Morel, Multiscale Model. Simul. 4(2), 490–530 (2005). DOI: 10.1137/040616024.

[11] Y. Zhou et al., 41, 74–86 (2016). DOI: 10.1016/j.jvcir.2016.09.007.

[12] S. A. Awate and R. T. Whitaker, Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, San Diego, CA, 20-25 June 2005.

[13] Y. Saad, Iterative Methods for Sparse Linear Systems (The Society for Industrial and Applied Mathematics, Siam, 2003).

[14] W. Yin et al., SIAM J. Imaging Sci. 1(1), 143–168 (2008). DOI: 10.1137/070703983.

[15] A. Beck and M. Teboulle, SIAM J. Imaging Sci. 2(1), 183–202 (2009). DOI: 10.1137/080716542.

[16] G. GhimpeĊ£eanu et al., IEEE Trans. Image Process. 25(1), 388–399 (2016). DOI: 10.1109/TIP.2015.2498413.

[17] L. Rudin, S. Osher, and E. Fatemi, Phys. D 60, 259–268 (1992). DOI: 10.1016/0167-2789(92)90242-F.

[18] ISO 17636-2, Non-Destructive Testing of Welds - Radiographic Testing - Part 2: X- and Gamma-Ray Techniques with Digital Detectors, International Organization for Standardization, Geneva, Switzerland (2013).

Metrics
Usage Shares
Total Views
22 Page Views
Total Shares
0 Tweets
22
0 PDF Downloads
0
0 Facebook Shares
Total Usage
22