A general method for multiresolutional analysis of mesoscale features in dark-field x-ray microscopy images

TL;DR

This paper introduces a multiresolution wavelet-based analysis method for dark-field x-ray microscopy images, enabling feature extraction, tracking, and focus determination across multiple length scales in mesoscale structures.

Contribution

The paper presents a general wavelet analysis framework tailored for DFXM images, demonstrating its effectiveness in feature extraction, pattern tracking, and image focus assessment.

Findings

Wavelet transforms effectively extract and track linear diffraction features.

Haar wavelet captures primary features across different regions.

The method can determine image focus from DFXM data.

Abstract

Dark-field x-ray microscopy utilizes Bragg diffraction to collect full-field x-ray images of "mesoscale" structure of ordered materials. Information regarding the structural heterogeneities and their physical implications is gleaned through the quantitative analyses of these images. Namely, one must be able to extract diffraction features that arise from lattice modulations or inhomogeneities, quantify said features, and identify and track patterns in the relevant quantitative properties in subsequent images. Due to the necessity to track features with a wide array of shapes and length scales while maintaining spatial resolution, wavelet transforms were chosen as a potent signal analysis tool. In addition to addressing multiple length scales, this method can be used in conjunction with other signal processing methods such as image binarization for increased functionality. In this article, we demonstrate three effective use of wavelet analyses pertaining to DFXM. We show how to extract and track smooth linear features-which are diffraction manifestations of twin boundaries-as the sample orientation changes as it is rotated about momentum transfer. Secondly, we show that even the simplest wavelet transform, the Haar transform, can be used to capture the primary features in DFXM images, over a range of length scales in different regions of interest within a single image enabling localized reconstruction. As a final application, we extend these techniques to determine when a DFXM image is in focus.