Application of Adaptive Anisotropic Diffusion Filter To Improve the Reflector Image Quality when Performing Ultrasonic Nondestructive Testing
Source abstract
Abstract
At present, systems for the recognition (classification) of reflectors based on artificial neural networks are being actively developed to increase the speed of preparation of the ultrasonic testing protocol and reduce the influence of the human factor. For these systems to operate most efficiently, the reflector images must be processed in order to increase the signal-to-noise ratio of the image and its segmentation (clustering). One way of segmentation is to process the image with an adaptive anisotropic diffusion filter, which is used to process optical images. The effectiveness of using this texture filter for segmentation of reflector images reconstructed from echo signals measured with antenna arrays has been demonstrated in model experiments.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Flow Measurement and Analysis · Image and Object Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (19)
Show source-provided references
1. Advances in Phased Array Ultrasonic Technology Applications, Waltham: Olympus NDT, 2007. https://www. olympus-ims.com/en/resources/books/. Accessed December 19, 2020.
2. Bazulin, E.G., Comparison of systems for ultrasonic nondestructive testing using antenna arrays or phased antenna arrays, Russ. J. Nondestr. Test., 2013, vol. 49, no. 7, pp. 404–423.10.1134/S1061830913070024 ↗
3. Bazulin, E.G., Determination of the reflector type from an image reconstructed using echo signals measured with ultrasonic antenna arrays, Russ. J. Nondestr. Test., 2014, vol. 50, no. 3, pp. 141–149.10.1134/S1061830914030036 ↗
4. Guo, Z., Ye, S., Wang, Y., and Lin, C., Resistance Welding Spot Defect Detection with Convolutional Neural Networks, ICVS 2017, Shenzhen, 2017, vol. 10528. https://doi.org/10.1007/978-3-319-68345-4_1510.1007/978-3-319-68345-4_15 ↗
5. Broomhead, D.S. and Lowe, D., Multivariable functional interpolation and adaptive networks, Complex Syst., 1988, vol. 2, pp. 321–355.
6. Perona, P. and Malik, J., Scale space and edge detection using anisotropic diffusion, IEEE Trans. Pattern Anal. Machine Intell., 1990, vol. 12, no. 6, pp. 629–639.10.1109/34.56205 ↗
7. Bezdek James C., Pattern Recognition with Fuzzy Objective Function Algorithms, Norwell: Kluwer Academic Publishers, 1981.10.1007/978-1-4757-0450-1 ↗
8. Haralick, R.M., Shanmugam, K., and Dinstein, I., Textural Features for Image Classification, IEEE Trans. Syst. Man Cybern., 1973, vol. SMC-3, no. 6, pp. 610—621. https://doi.org/10.1109/TSMC.1973.430931410.1109/TSMC.1973.4309314 ↗
9. Tizhoosh, H.R., Type II fuzzy image segmentation, in Fuzzy Sets and Their Extensions: Representation, Aggregation and Models, Bustince, H., Herrera, F., and Montero, J., Eds., Berlin: Springer, 2008. https://doi.org/10.1007/978-3-540-73723-0_3110.1007/978-3-540-73723-0_31 ↗
10. Otsu, N., A Threshold Selection Method from Gray-Level Histograms, IEEE Trans. Pattern Anal. Machine Intell., 1979, vol. 9, no. 1, pp. 62–66.
11. Neurohive, Deep Claster. https://neurohive.io/ru/papers/deep-claster/. Accessed: December 19, 2020.
12. Zuiderveld, K., Contrast limited adaptive histogram equalization, in Graphics gems IV, San Diego: Academic Press Professional, 1994, pp. 474–485.
13. Samarskiy, A.A. and Gulin, A.V., Chislennie metodi: Uchebnoe posobie dlya vuzov (Numerical Methods. A Textbook for Universities), Moscow: Nauka, 1989.
14. Borroto-Fernández, M., González-Hidalgo, M., and León-Mecías, A., New estimation method of the contrast parameter for the Perona–Malik diffusion equation, Comput. Meth. Biomech. Biomed. Eng.: Imaging Visualization, 2014. https://doi.org/10.1080/21681163.2014.97428910.1080/21681163.2014.974289 ↗
15. Ben Mhamed, I., Abid, S., and Fnaiech, F., Weld defect detection using a modified anisotropic diffusion model, EURASIP J. Adv. Signal Process., 2012, vol. 46. https://doi.org/10.1186/1687-6180-2012-4610.1186/1687-6180-2012-46 ↗
16. Artyukov, I.A. and Irtuganov, N.N., Noise-Driven Anisotropic Diffusion Filtering For X-Ray Low Contrast Imaging, J. Russ. Laser Res., 2019, no. 3, pp. 150–154. https://doi.org/10.1007/s10946-019-09782-810.1007/s10946-019-09782-8 ↗
17. Research and Production Center “ECHO+” company website. http://www.echoplus.ru. Accessed: January 3, 2021.
18. Bazulin, E.G., Reconstruction of reflector images using the C-SAFT method with account for the anisotropy of the material of the test object, Russ. J. Nondestr. Test., 2015, vol. 51, no. 4, pp. 217–226.10.1134/S1061830915040026 ↗
19. Bazulin, E.G. and Kokolev, S.A., Increasing the signal-to-noise ratio in ultrasonic testing of repair welds using the technology of thinned antenna arrays, Russ. J. Nondestr. Test., 2013, vol. 49, no. 5, pp. 283–293.10.1134/S1061830913050021 ↗