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

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Аннотация

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.

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Авторлар туралы

V. Bondarenko

National Research Center “Kurchatov Institute”

Хат алмасуға жауапты Автор.
Email: bondarenko.v@crys.ras.ru

Shubnikov Institute of Crystallography of the Kurchatov Complex Crystallography and Photonics

Ресей, Moscow

S. Rekhviashvili

National Research Center “Kurchatov Institute”

Email: bondarenko.v@crys.ras.ru

Shubnikov Institute of Crystallography of the Kurchatov Complex Crystallography and Photonics

Ресей, Moscow

F. Chukhovskii

National Research Center “Kurchatov Institute”; Kabardin-Balkar Scientific Center of Russian Academy of Sciences

Email: bondarenko.v@crys.ras.ru

Shubnikov Institute of Crystallography of the Kurchatov Complex Crystallography and Photonics, Institute of Applied Mathematics and Automation

Ресей, Moscow; Nalchik

Әдебиет тізімі

  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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Әрекет
1. JATS XML
Жүктеу
2. Fig. 1. Functions in the D4 transform.

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3. Fig. 2. RMS as a function of the parameter ε. 1D-controlled filter, filter window size ρ = 1, a – full image, b – area near the defect. Filter options: 1 – noisy image is used as a reference image; 2 – reference image coincides with the exact image; 3 – reference image is generated automatically; 4 – interpolation, ρ = 2.

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4. Fig. 3. RMS as a function of the parameter ε. 2D-controlled filter, filter window size ρ = 1, a – full image, b – area near the defect. Filter options: 1 – noisy image is used as a reference image; 2 – reference image coincides with the exact image; 3 – reference image is generated automatically; 4 – interpolation, ρ = 2.

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5. Fig. 4. 2D images: a – accurate, b – noisy, c – filtered, 2D-controlled filter, automatically generated reference image.

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6. Fig. 5. 2D images: a – accurate, b – noisy. Wavelet filtering: c – algorithm of the first type [7], d – algorithm of the second type.

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7. Fig. 6. Image intensity profiles taken along a segment passing horizontally through the center of the defect. Solid line – accurate image, dotted line – noisy image, dotted line – filtering result: a – for a controlled filter with automatic generation of reference image, b – algorithm of the first type of wavelet filtering (∆ = 80), c – algorithm of the second type of wavelet filtering (∆ = 80).

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