Application of image processing to STEM tomography of low-contrast materials

In this study, the effect of various image-processing techniques on the visibility of tomographic reconstructions is investigated for a low-contrast material system of non-uniform thickness containing complex features such as grain boundaries and nanoparticles. Starting with a tilt series of high-angle annular dark-field (HAADF) images from an area of Dy-doped YBa2Cu3O7-x-coated superconductor obtained using a scanning transmission electron microscope, various image-processing techniques were applied. These can be classified as edge detection, contrast-enhancing methods for non-uniform thickness and image sharpening. Although the processing methods violate the projection criterion for tomographic reconstruction, they were found, at least in this case, to enhance contrast and define the correct shape and size of structural features with minimal artifacts. Enhancing the visibility of structural features in this way allows the spatial distribution of the nanoparticles, their size, number density and location relative to each other and grain boundaries to be determined, which are essential to understand the flux-pinning characteristics of these materials.