Compressive Scanning Transmission Electron Microscopy

TL;DR

This paper introduces a novel Compressive Sensing-based scanning method for STEM that reduces electron beam damage by subsampling probe locations and reconstructing high-quality images with Bayesian dictionary learning, validated through real experiments.

Contribution

It presents a new CS-based scanning approach for STEM that minimizes electron damage while maintaining image quality, validated with real data and advanced reconstruction techniques.

Findings

Significant reduction in electron beam damage.

Successful image reconstruction from subsampled data.

Feasibility demonstrated with real experimental data.

Abstract

Scanning Transmission Electron Microscopy (STEM) offers high-resolution images that are used to quantify the nanoscale atomic structure and composition of materials and biological specimens. In many cases, however, the resolution is limited by the electron beam damage, since in traditional STEM, a focused electron beam scans every location of the sample in a raster fashion. In this paper, we propose a scanning method based on the theory of Compressive Sensing (CS) and subsampling the electron probe locations using a line hop sampling scheme that significantly reduces the electron beam damage. We experimentally validate the feasibility of the proposed method by acquiring real CS-STEM data, and recovering images using a Bayesian dictionary learning approach. We support the proposed method by applying a series of masks to fully-sampled STEM data to simulate the expectation of real CS-STEM. Finally, we perform the real data experimental series using a constrained-dose budget to limit the impact of electron dose upon the results, by ensuring that the total electron count remains constant for each image.