Experimental data

By simultaneously achieving high spatial and angular sampling resolution, four dimensional scanning transmission electron microscopy (4D STEM) is enabling new analysis techniques that provide unprecedented insight into the atomic structure of materials. Applying these techniques to scientifically and technologically significant beam-sensitive materials remains challenging because the low doses needed to minimise beam damage lead to noisy data. We demonstrate an unsupervised deep learning model that leverages the continuity and coupling between the probe position and the electron scattering distribution to denoise 4D STEM data: By restricting the network complexity it can learn the geometric flow present but not the noise. Through experimental and simulated case studies, we demonstrate that denoising as a preprocessing step enables 4D STEM analysis techniques to succeed at lower doses, broadening the range of materials that can be studied using these powerful structure characterization techniques.This repository contains the experimental datasets collected for the paper "Unsupervised deep denoising for four-dimensional scanning transmission electron microscopy"

通过同时实现高空间与角度采样分辨率，四维扫描透射电子显微镜（4D STEM）可催生全新分析技术，为解析材料的原子结构提供前所未有的深入洞察。将这些技术应用于兼具科学与技术重要性的束流敏感材料时仍面临挑战：由于需采用低辐照剂量以最小化束流损伤，这会导致数据噪声显著增加。本研究提出一种无监督深度学习模型，通过利用探针位置与电子散射分布间的连续性与耦合关系对4D STEM数据进行降噪：通过限制网络复杂度，该模型仅能学习数据中蕴含的几何流，而非噪声信号。通过实验与模拟案例研究，我们验证了将降噪作为预处理步骤后，4D STEM分析技术可在更低辐照剂量下顺利开展，从而拓宽了可采用这类高效结构表征技术开展研究的材料范畴。本仓库包含为本论文"Unsupervised deep denoising for four-dimensional scanning transmission electron microscopy"所收集的实验数据集。