用多视角融合技术，实现超低剂量下原子级分辨率成像
4D-MISR: A unified model for low-dose super-resolution imaging via feature fusion
- 通过融合多张低分辨、亚像素偏移图像重建高分辨结果
- 在超低剂量下达到与传统全息术相当的原子级分辨率
- 适用于蛋白质、二维材料等易损样本，适合材料结构分析
电子显微镜虽能提供原子级结构信息，但辐射损伤限制其在蛋白质和二维材料等束敏感材料中的应用。为突破这一瓶颈，我们借鉴遥感领域多图像超分辨（MISR）原理，提出4D-MISR方法：将多张低分辨率、亚像素偏移的图像进行融合，并利用集成合成多角度观测特征的卷积神经网络增强重建效果。设计了双路径注意力引导网络，实现了从超低剂量4D-STEM数据中获得原子级超分辨图像，在非晶、半结晶和结晶态束敏感样品上均表现出稳健的原子级可视化能力。在代表性材料上的系统评估表明，该方法在超低剂量条件下可达到与传统扫描透射电镜全息术相当的空间分辨率。本工作拓展了4D-STEM的应用边界，为辐射脆弱材料的结构分析提供了通用新方法。
原文摘要 · Abstract (English)
While electron microscopy offers crucial atomic-resolution insights into structure-property relationships, radiation damage severely limits its use on beam-sensitive materials like proteins and 2D materials. To overcome this challenge, we push beyond the electron dose limits of conventional electron microscopy by adapting principles from multi-image super-resolution (MISR) that have been widely used in remote sensing. Our method fuses multiple low-resolution, sub-pixel-shifted views and enhances the reconstruction with a convolutional neural network (CNN) that integrates features from synthetic, multi-angle observations. We developed a dual-path, attention-guided network for 4D-STEM that achieves atomic-scale super-resolution from ultra-low-dose data. This provides robust atomic-scale visualization across amorphous, semi-crystalline, and crystalline beam-sensitive specimens. Systematic evaluations on representative materials demonstrate comparable spatial resolution to conventional ptychography under ultra-low-dose conditions. Our work expands the capabilities of 4D-STEM, offering a new and generalizable method for the structural analysis of radiation-vulnerable materials.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。