Computer diffraction tomography. Digital image processing and analysis based on the 1D-, 2D-sized guided and wavelet-function filter processing.

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Abstract

One presents and analyzes the results of computer processing for a plane-wave X-ray topography imaging of a point defect of the Coulomb-types in the Si(111) crystal recorded by an X-ray detector against a background of the Gaussian noise, and their subsequent filtering by using the 1D-, 2D-sized guided and a heuristic wavelet 4th-order Daubechie’s atomic function. The filtering efficiency of a topography image is determined by the parameter of the averaged over all pixels relative square deviations of the pixel intensities (RMS.) of the processed and reference (noise-free) 2D image. Practical methods for selecting filtration parameters are proposed, using which the considered methods work well enough to be used in practice for the noise processing of plane-wave X-ray topography images, meaning their use for the 3D digital recovering nanosized crystal defects.

About the authors

V. I. Bondarenko

Shubnikov Institute of Crystallography of the Kurchatov Complex Crystallography and Photonics of the NRC “Kurchatov Institute”

Email: bondarenko.v@crys.ras.ru
Russian Federation, Moscow, 119333

S. S. Rekhviashvili

Institute of Applied Mathematics and Automation KBSC RAS

Email: bondarenko.v@crys.ras.ru
Russian Federation, Nalchik, 360000

F. N. Chukhovskii

Shubnikov Institute of Crystallography of the Kurchatov Complex Crystallography and Photonics of the NRC “Kurchatov Institute”; Institute of Applied Mathematics and Automation KBSC RAS

Author for correspondence.
Email: bondarenko.v@crys.ras.ru
Russian Federation, Moscow, 119333; Nalchik, 360000

References

  1. Authier A. Dynamical Theory of X-ray Diffraction. New York: Oxford University Press, 2001. 680 p.
  2. Asadchikov V., Buzmakov A., Chukhovskii F. et al. // J. Appl. Cryst. 2018. V. 51. P. 1616. https://doi.org/10.1107/S160057671801419X
  3. Бондаренко В.И., Конарев П.В., Чуховский Ф.Н. // Кристаллография. 2020. Т. 65. № 6. С. 845. https://doi.org/10.31857/S0023476120060090
  4. Chukhovskii F.N., Konarev P.V., Volkov V.V. // Acta Cryst. A. 2020. V. 76. P. 16. https://doi.org/10.1107/S2053273320000145
  5. Hendriksen A.A., Bührer M., Leone L. et al. // Sci. Rep. 2021. V. 11. P. 11895. https://doi.org/10.1038/s41598-021-91084-8
  6. Chukhovskii F.N., Konarev P.V., Volkov V.V. // Crystals. 2024. V. 14. P. 29. https://doi.org/10.3390/cryst14010029
  7. Бондаренко В.И., Рехвиашвили C.Ш., Чуховский Ф.Н. // Кристаллография. 2024. Т. 69. № 5. С. 755. https://doi.org/10.31857/S0023476124050012
  8. Welstead S. Fractal and Wavelet Image Compression Techniques. SPIE Publications, 1999. 254 p.
  9. He K., Sun J., Tang X. // IEEE Trans. Pattern Anal. Machine Intell. 2013. V. 35. № 6. P. 1397. https://doi.org/10.1109/TPAMI.2012.213
  10. Nagajyothi G., Raghuveera E. // Int. J. Adv. Res. Electron. Commun. Eng. 2016. V. 5. P. 2362.
  11. Li Z., Zheng J., Zhu Z. et al. // IEEE Trans. Image Process. 2015. V. 24. P. 120. https://doi.org/10.1109/TIP.2014.2371234
  12. Zhang Y.Q., Ding Y., Liu J. // IET Image Process. 2013. V. 7. № 3. P. 270. https://doi.org/10.1049/iet-ipr.2012.0351
  13. Zhu S., Yu Z. // IET Image Process. 2020. V. 14. № 11. P. 2561. https://doi.org/10.1049/iet-ipr.2019.1471
  14. Малла С. Вейвлеты в обработке сигналов. М.: Мир, 2005. 671 с.
  15. Гонсалес Р., Вудс Р. Цифровая обработка изображений. М.: Техносфера, 2005. 1072 с.
  16. Дремин И.М., Иванов О.В., Нечитайло В.А. // Успехи физ. наук. 2001. Т. 171. № 5. С. 465. https://doi.org/10.3367/UFNr.0171.200105a.0465
  17. Уэлстид С. Фракталы и вейвлеты для сжатия изображений в действии. М.: Триумф, 2003. 320 с.

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In the print version, the article was published under the DOI: 10.31857/S0023476125040029


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