By simultaneously achieving high spatial and angular sampling resolution, four dimensional scanning transmission electron microscopy (4D STEM) is enabling analysis techniques that provide great 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.

通过同时实现高空间和角度采样分辨率，四维扫描透射电子显微镜 （4D STEM） 使分析技术能够深入了解材料的原子结构。将这些技术应用于具有科学和技术意义的电子束敏感材料仍然具有挑战性，因为最大限度地减少电子束损伤所需的低剂量会导致数据噪声。我们演示了一个无监督深度学习模型，该模型利用探针位置和电子散射分布之间的连续性和耦合性来对 4D STEM 数据进行降噪。通过限制网络复杂性，它可以学习存在的几何流，但不能学习噪声。通过实验和模拟案例研究，我们证明了去噪作为预处理步骤使 4D STEM 分析技术能够在较低剂量下取得成功，从而拓宽了可以使用这些强大的结构表征技术研究的材料范围。