Abstract
Scanning Transmission Electron Microscopy (STEM) enables the observation of atomic arrangements at sub-angstrom resolution, allowing for atomically resolved analysis of the physical and chemical properties of materials. However, due to the effects of noise, electron beam damage, sample thickness, etc, obtaining satisfactory atomic-level images is often challenging. Enhancing STEM images can reveal clearer structural details of materials. Nonetheless, existing STEM image enhancement methods usually overlook unique features in the frequency domain, and existing datasets lack realism and generality. To resolve these issues, in this paper, we develop noise calibration, data synthesis, and enhancement methods for STEM images. We first present a STEM noise calibration method, which is used to synthesize more realistic STEM images. The parameters of background noise, scan noise, and pointwise noise are obtained by statistical analysis and fitting of real STEM images containing atoms. Then we use these parameters to develop a more general dataset that considers both regular and random atomic arrangements and includes both HAADF and BF mode images. Finally, we design a spatial-frequency interactive network for STEM image enhancement, which can explore the information in the frequency domain formed by the periodicity of atomic arrangement. Experimental results show that our data is closer to real STEM images and achieves better enhancement performances together with our network. Code will be available at this https URL}{this https URL.
Abstract (translated)
扫描透射电子显微镜（STEM）能够以亚埃级分辨率观察材料的原子排列，从而实现对物质物理和化学性质的原子级别分析。然而，由于噪声、电子束损伤、样品厚度等因素的影响，获取满意的原子级图像往往具有挑战性。增强STEM图像可以揭示材料更清晰的结构细节。不过，现有的STEM图像增强方法通常忽视了频域中的独特特征，并且现有数据集缺乏真实性和普遍性。为解决这些问题，在本文中，我们开发了一种用于STEM图像的噪声校准、数据合成和增强的方法。 首先，我们提出一种STEM噪声校准方法，该方法被用来合成更逼真的STEM图像。通过统计分析和拟合含有原子的真实STEM图像中的背景噪声、扫描噪声及点噪声参数来获得这些参数。然后利用这些参数开发了一个更具普遍性的数据集，这个数据集考虑了规则和随机的原子排列，并包括高角度环形暗场（HAADF）模式和背散射（BF）模式下的图像。 最后，我们设计了一种空间频域互动网络用于STEM图像增强，该方法可以探索由原子排列周期性形成的频率域中的信息。实验结果显示我们的数据更接近真实的STEM图像，并且与我们开发的网络一起实现了更好的增强性能。代码可在[此处](https://this https URL)获取。
URL
https://arxiv.org/abs/2504.02555
