International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 4, No. 6, 8 Jul. 2012
Cover page and Table of Contents: PDF (size: 394KB)
A Novel Algorithm for De-Noising Radiographic Images
Full Text (PDF, 394KB), PP.22-28
Views: 0 Downloads: 0
Author(s)
Index Terms
Two Dimensional Left Median Filter, Median Filter, Weld, Radiographic Image
Abstract
The radiographic image has low contrast and high noise. In order to improve the image for observation and accurate analysis, various digital image processing techniques can be applied. In this research we propose Two Dimensional Left Median Filter method for de-noising radiographic images of welding. We have used the measures Peak Signal-to-Noise Ratio and the Mean Absolute Error for comparison. The accuracy of results obtained through our method is better than the Median and Mean Filter methods.
Cite This Paper
Alireza Azarimoghaddam,Lalitha Rangarajan,"A Novel Algorithm for De-Noising Radiographic Images", IJIGSP, vol.4, no.6, pp.22-28, 2012. DOI: 10.5815/ijigsp.2012.06.04
Reference
[1]http://www.wikipedia.org.
[2]AP. Rale, DC. Gharpure, and VR. Ravindran. Comparison of different ANN techniques for automatic defect detection in X-Ray images. International Conference of Emerging Trends in Electronic and Photonic Devices & Systems, Varanasi ,pp. 193-197, 2009.
[3]Fu, M., Sun, J., Zhong, S., and Zou, C. “An iterative filtering algorithm based on signaling game idea”. International Conference on Computer and Communication Technologies in Agriculture Engineering, 2010, pp. 17-20.
[4]A. Aboshosha, M. Hassan, M. Ashour, and M. El Mashade. “Image denoising based on spatial filters, an analytical study”. IEEE, 2010, pp. 245-250.
[5]S. Liu, L. Chen, X. Fan, Z. Qu, and X. Yang, X. “Combining Pseudo-median filter and median filter to improve performance”. IEEE, 2010, pp. 513-517.
[6]S. J. Ko, T. M. Forest. Image sequence enhancement based on adaptive symmetric order statistics. Circuits and Systems II: Analog and Digital Signal Processing. IEEE Transactions on, 1993,Vol. 40(8), pp. 504-509.
[7]J. Jiang; J. Shen. “An Effective Adaptive Median Filter Algorithm for Removing Salt & Pepper Noise in Images”. IEEE, 2010, pp. 1-4.
[8]G. Wang, T. W. Liao. “Automatic identification of different types of welding defects in radiographic images”. NDT & E International, 2002, Vol. 35(8), pp. 519-528.
[9]R.R. Da Silva,M.H.S. Siqueira, L.P. Caloba, I.C. Da Silva, A. De Carvalho, and J. Rebello. “Contribution To The Development Of A Radiographic Inspection Automated System”. Journal of Nondestructive Testing, 2002, Vol. 7(12) , pp. 1-8.
[10]M. Carrasco, D. Mery. “Segmentation of welding defects using a robust algorithm”. Materials Evaluation, 2004, Vol. 62(11) , pp. 1142-1147.
[11]G. Padua, R. Silva, M. Siqueira, J. Rebello, L. Caloba, L. and R. De Janeiro. Classification of welding defects in radiographs using traversal profiles to the weld seam. 16th World Conference on Nondestructive Testing, 2004.
[12]N. Nacereddine, M. Zelmat, S. Belaifa, and M. Tridi. “Weld defect detection in industrial radiography based digital image processing”. World Academy of Science, Engineering and Technology, PWASET, 2005, Vol. 2, pp. 145-148.
[13]S. Alghalandis, G. Alamdari. Welding defect pattern recognition in radiographic images of gas pipelines using adaptive feature extraction method and neural network classifier. 23rd World Gas Conference, Amesterdam, 2006.
[14]T. Nikiforova, N. Fedotov. “Methods of stochastic geometry in recognition of weld defects”. Pattern Recognition and Image Analysis, 2006, Vol. 16(1) , pp. 12-14.
[15]A. Bovik .Handbook of Image and Video Processing. 2ed ed. New York: Elsevier Academic Press, 2000.
[16]Q. Huynh-Thu, M. Ghanbari. “Scope of validity of PSNR in image/video quality assessment”. Electronics letters, 2008, Vol. 44(13), pp. 800-801.
[17]R. H. Chan, C.W. Ho, and M. Nikolova, M. “Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization”. Image Processing, IEEE Transactions, 2005, Vol. 14(10) , pp. 1479-1485.
[18]M. Nikolova. “A variational approach to remove outliers and impulse noise”. Journal of Mathematical Imaging and Vision, 2004, Vol. 20(1) , pp. 99- 120.