Abstract
A method for restoration of ultrasonic C-scan images is presented by using a radial basis function network. The method attempts to reproduce the mapping between the degraded C-scan image and the high quality one by training a RBF network. The inputs for training are the sub-images divided from C-scan image of flat-bottom hole of size 3mm and the output is the corresponding center in high quality image. After the network was trained, the other C-scan images were used to verify the network. The results show that the network produces good restored results, in which the noise is removed and the edges are deblurred. Comparing the restored results by the networks trained by the different sub-images, the sub-images with size 7x7, scanning step of 3 are determined as the optimal inputs for training.
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© 2004 Springer-Verlag Berlin Heidelberg
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Cao, Z., Chen, H., Xue, J., Wang, Y. (2004). Ultrasonic C-scan Image Restoration Using Radial Basis Function Network. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_63
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DOI: https://doi.org/10.1007/978-3-540-28648-6_63
Publisher Name: Springer, Berlin, Heidelberg
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