Joint denoising and distortion correction of atomic scale scanning transmission electron microscopy images
This work addresses the challenge of precise atom localization in materials science, which is crucial for analyzing material properties, but it is incremental as it builds on existing methods for distortion correction.
The researchers tackled the problem of random image distortions in atomic-scale scanning transmission electron microscopy (STEM) images, which hinder precise atom localization, by developing a Bayesian method that jointly estimates distortions and reconstructs the atomic grid, achieving improved accuracy in synthetic and real data evaluations.
Nowadays, modern electron microscopes deliver images at atomic scale. The precise atomic structure encodes information about material properties. Thus, an important ingredient in the image analysis is to locate the centers of the atoms shown in micrographs as precisely as possible. Here, we consider scanning transmission electron microscopy (STEM), which acquires data in a rastering pattern, pixel by pixel. Due to this rastering combined with the magnification to atomic scale, movements of the specimen even at the nanometer scale lead to random image distortions that make precise atom localization difficult. Given a series of STEM images, we derive a Bayesian method that jointly estimates the distortion in each image and reconstructs the underlying atomic grid of the material by fitting the atom bumps with suitable bump functions. The resulting highly non-convex minimization problems are solved numerically with a trust region approach. Well-posedness of the reconstruction method and the model behavior for faster and faster rastering are investigated using variational techniques. The performance of the method is finally evaluated on both synthetic and real experimental data.