Framework of compressive sensing and data compression for 4D-STEM

TL;DR

This paper introduces a novel reconstruction and compression framework for 4D-STEM data that significantly reduces data size and improves efficiency while maintaining high imaging fidelity, enabling broader application of the technique.

Contribution

The authors develop a general subsampling and reconstruction method for 4D-STEM that enhances data and dose efficiency, with demonstrated experimental validation and substantial data compression capabilities.

Findings

Achieves over 100-fold data size reduction while preserving image features

Effective subsampling in real and reciprocal space for high-fidelity reconstruction

Demonstrates practical application with experimental random scan data

Abstract

Four-dimensional Scanning Transmission Electron Microscopy (4D-STEM) is a powerful technique for high-resolution and high-precision materials characterization at multiple length scales, including the characterization of beam-sensitive materials. However, the field of view of 4D-STEM is relatively small, which in absence of live processing is limited by the data size required for storage. Furthermore, the rectilinear scan approach currently employed in 4D-STEM places a resolution- and signal-dependent dose limit for the study of beam sensitive materials. Improving 4D-STEM data and dose efficiency, by keeping the data size manageable while limiting the amount of electron dose, is thus critical for broader applications. Here we develop a general method for reconstructing 4D-STEM data with subsampling in both real and reciprocal spaces at high fidelity. The approach is first tested on the subsampled datasets created from a full 4D-STEM dataset, and then demonstrated experimentally using random scan in real-space. The same reconstruction algorithm can also be used for compression of 4D-STEM datasets, leading to a large reduction (100 times or more) in data size, while retaining the fine features of 4D-STEM imaging, for crystalline samples.