Joint denoising and distortion correction of atomic scale scanning transmission electron microscopy images

TL;DR

This paper introduces a Bayesian method for joint denoising and distortion correction in atomic-scale STEM images, enabling precise atomic localization despite specimen movements and image distortions.

Contribution

It presents a novel joint estimation approach for image distortion correction and atomic grid reconstruction in STEM images, addressing challenges from rastering and specimen movement.

Findings

Effective distortion correction on synthetic data

Accurate atomic grid reconstruction on real data

Robustness to faster rastering speeds

Abstract

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.