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Application of Fourier Theory in Radiographic Image Analysis

Digital radiographic images are considered two-dimensional signals, where the signals are represented by gray value matrices. When such signals are properly presented in the form of surfaces, areas of large signal magnitude appear bright and areas of small magnitude appear dark. Fourier transforms of radiographic images transfer the signal from the spatial to the frequency domain, where each point in the frequency domain represents a particular frequency contained in the radiographic image. In the Fourier domain, low frequencies are related to the background of the radiographic image, while high frequencies represent edge details of the images. Fourier analysis is an important tool in radiographic image processing, which can be used to separate low and high frequency details of an image. In this paper, two-dimensional Fourier transforms of signals are used to analyze and improve radiographic images.

References
ASNT, Nondestructive Testing Handbook, Second Edition: Volume 3, Radiography and Radiation Testing, Columbus, Ohio, American Society for Nondestructive Testing, 1985. Bremaud, Pierre, Mathematical Principles of Signal Processing, New York, Springer-Verlag, 2002. Castleman, K., Digital Image Processing, New York, Oxford, 1996. Das, Sanjoy, P.R. Vaidya and B.K. Shah, “Image Processing Techniques for Digital Radiographic Images,” Materials Evaluation, Vol. 64, 2006, pp. 498–501. Jain, A.K., Fundamental of Digital Image Processing, Englewood Cliffs, New Jersey, Prentice Hall, 1989. Kim, Sang Ho and Jan P. Allebach, “Optimal Unsharp Mask for Image Sharpening and Noise Removal,” Journal of Electronic Imaging, Vol. 14, 2005, pp. 1–13. Wanga, Xin and Brian Stephen Wong, “Image Enhancement for Radiography Inspection,” NDT.net, Vol. 10, 2005, available at www.ndt .net/article/icem2004/papers/64/64.htm.
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