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X-ray Crystallography

X-ray Crystallography

The size of the unit cell and the arrangement of atoms in a crystal may be determined from measurements of the diffraction of X-rays by the crystal, termed X-ray crystallography.
Diffraction
Diffraction is the change in the direction of travel experienced by an electromagnetic wave when it encounters a physical barrier whose dimensions are comparable to those of the wavelength of the light. X-rays are electromagnetic radiation with wavelengths about as long as the distance between neighboring...

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Patching-based deep-learning model for the inpainting of Bragg coherent diffraction patterns affected by detector

Matteo Masto^1,2, Vincent Favre-Nicolin^1,2, Steven Leake¹

¹ESRF, The European Synchrotron, 71 Avenue des Martyrs, Grenoble, France.

Journal of Applied Crystallography

|August 7, 2024

Summary

A deep-learning algorithm effectively fills detector gaps in Bragg coherent diffraction imaging (BCDI) data. This method removes artifacts, improving the accuracy of reconstructed images for high-resolution experiments.

Keywords:

Bragg coherent diffraction imaging deep learning image inpainting

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Area of Science:

Physics
Materials Science
Computational Science

Background:

Bragg coherent diffraction imaging (BCDI) is crucial for nanoscale material characterization.
Detector gaps in BCDI data introduce significant artifacts, compromising reconstruction accuracy.
Restoring missing intensity data is essential for reliable BCDI analysis.

Purpose of the Study:

To develop a deep-learning algorithm for inpainting missing intensity data in BCDI patterns.
To mitigate artifacts caused by detector gaps in BCDI reconstructions.
To enhance the reliability and accuracy of BCDI experiments, especially at high resolutions.

Main Methods:

A deep-learning algorithm was trained using cropped sections of diffraction data.
The neural network predicts intensity values for regions affected by detector gaps.
A patching strategy was employed to combine predictions and complete diffraction peaks.

Main Results:

The deep-learning method successfully inpainted detector gaps in BCDI patterns.
Gap-induced artifacts were effectively removed from reconstructed objects.
The approach demonstrated robustness on both simulated and experimental datasets.
The method is scalable to experimental data arrays of any size.

Conclusions:

The proposed deep-learning algorithm provides a robust solution for BCDI data inpainting.
This technique significantly improves the quality of reconstructed objects from BCDI data.
The method is vital for advancing high-resolution BCDI applications by enabling the use of more complete datasets.