Denoising of the Poisson-Noise Statistics 2D Image Patterns in the Computer X-ray Diffraction Tomography

Abstract

A central point of validity of computer X-ray diffraction micro tomography is to improve the digital contrast and spatial resolution of the 3D-recovered nano-scaled objects in crystals. In this respect, the denoising issue of the 2D image patterns data involved in the 3D high-resolution recovery processing has been treated. The Poisson-noise simulation of 2D image patterns data was performed; afterwards, it was employed for recovering nano-scaled crystal structures. By using the statistical average and geometric means methods of the acquired 2D image frames, we showed that the statistical average hypothesis works well, at least in the case of 2D Poisson-noise image data related to the Coulomb-type point defect in a crystal Si(111). The validation of results related to the de-noised 2D IPs data obtained was carried out by both the 3D recovery processing of the Coulomb-type point defect in a crystal Si(111) and using the peak signal-to-noise ratio (PSNR) criterion.

Keywords

• high-resolution X-ray diffraction microtomography
• Coulomb-type point defects in a crystal
• statistical noise filtering
• signal-to-noise ratio
• χ²-target function
• figure of merit