Article Periodicals » Materials Evaluation » Article
Detecting Discontinuities in Foggy Radiographs of Welded Objects Using ℓ1 − ℓ0 Minimization

Industrial radiography can help to detect discontinuities in welded objects, but the technique suffers some major disadvantages related to fog and noise, and there is also a need to impose some further processing to detect the discontinuities in the images. The dynamic range of foggy radiographs is narrow, making it difficult to maximize the contrast of an image. In this paper, we utilize an ℓ1 − ℓ0 minimization methodology that deconstructs X-ray images into a base layer and a detail layer. Afterward, to reconstruct the denoised image, an ℓ1 − ℓ0 reconstruction optimization problem will be solved. These results show that the algorithm reduces the foggy parts of radiographs in the base layer and enhances the structure in the detail layer. When compared with the original image, a clearer image can be produced, which can be used for discontinuity detection. Also, the reconstructed images show a very clear and visible discontinuity region in the radiographs.

References

Boyd, S., N. Parikh, E. Chu, B. Peleato, and J. Eckstein, 2010, “Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers,” Foundations and Trends® in Machine Learning, Vol. 3, No. 1, doi: 10.1561/2200000016.

Durand, F., and J. Dorsey, 2002, “Fast Bilateral Filtering for the Display of High-Dynamic-Range Images,” ACM Transactions on Graphics (TOG), Vol. 21, No. 3, pp. 257–266.

Eckstein, J, 2012, “Augmented Lagrangian and Alternating Direction Methods for Convex Optimization: A Tutorial and Some Illustrative Computational Results,” RUTCOR Research Reports 32-2012, Rutgers Center for Operations Research, Rutgers University, Piscataway, NJ, accessed at rutcor.rutgers.edu/pub/rrr/reports2012/32_2012.pdf.

Edalati, K., N. Rastkhah, A. Kermani, M. Seiedi, and A. Movafeghi, 2006, “The Use of Radiography for Thickness Measurement and Corrosion Monitoring in Pipes,” International Journal of Pressure Vessels and Piping, Vol. 83, No. 10, pp. 736–741.

Friedman, J., T. Hastie, and R. Tibshirani, 2008, “Sparse Inverse Covariance Estimation with the Graphical Lasso,” Biostatistics, Vol. 9, No. 3, pp. 432–441.

Goldstein, T., B. O’Donoghue, S. Setzer, and R. Baraniuk, 2014, “Fast Alternating Direction Optimization Methods,” SIAM Journal on Imaging Sciences, Vol. 7, No. 3, pp. 1588–1623.

Kohlberger, T., C. Schnorr, A. Bruhn, and J. Weickert, 2005, “Domain Decomposition for Variational Optical-Flow Computation,” IEEE Transactions on Image Processing, Vol. 14, No. 8, pp. 1125–1137.

Li, X., S.K. Tso, X.-P. Guan, and Q. Huang, 2006, “Improving Automatic Detection of Defects in Castings by Applying Wavelet Technique,” IEEE Transactions on Industrial Electronics, Vol. 53, No. 6, pp. 1927–1934.

Liang, Z., J. Xu, D. Zhang, Z. Cao, and L. Zhang, 2018, “A Hybrid ℓ1-ℓ0 Layer Decomposition Model for Tone Mapping,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4758–4766.

Lu, T., P. Neittaanmäki, and X.-C. Tai, 1992, “A Parallel Splitting-Up Method for Partial Differential Equations and Its Applications to Navier-Stokes Equations,” ESAIM: Mathematical Modelling and Numerical Analysis (ESAIM: M2AN), Vol. 26, No. 6, pp. 673–708.

Mery, D., V. Riffo, U. Zscherpel, G. Mondragón, I. Lillo, I. Zuccar, H. Lobel, and M. Carrasco, 2015, “GDXray: The Database of X-ray Images For Nondestructive Testing,” Journal of Nondestructive Evaluation, Vol. 34, No. 42, doi: 10.1007/s10921-015-0315-7.

Mix, P.E., 2005, Introduction to Nondestructive Testing: A Training Guide, 2nd ed., John Wiley & Sons Inc., Hoboken, NJ.

Movafeghi, A., N. Mohammadzadeh, E. Yahaghi, J. Nekouei, P. Rostami, and G. Moradi, 2018, “Defect Detection of Industrial Radiography Images of Ammonia Pipes by a Sparse Coding Model,” Journal of Nondestructive Evaluation, Vol. 37, No. 3, doi:org/10.1007/s10921-017-0458-9.

Parikh, N., and S. Boyd, 2014, “Proximal Algorithms,” Foundations and Trends® in Optimization, Vol. 1 No. 3, pp. 127–239.

Tai, X.-C., and Y. Duan, 2011, “Domain Decomposition Methods with Graph Cuts Algorithms for Image Segmentation,” International Journal of Numerical Analysis and Modeling, Vol. 8, No. 1, pp. 137–155.

Xu, J., X.-C. Tai, and L.-L. Wang, 2010, “A Two-Level Domain Decomposition Method for Image Restoration,” Inverse Problems & Imaging, Vol. 4, No. 3, pp. 523–545.

Metrics
Usage Shares
Total Views
25 Page Views
Total Shares
0 Tweets
25
0 PDF Downloads
0
0 Facebook Shares
Total Usage
25