学术文献库

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.