用物理引导的合成数据训练模型，修复扫描隧穿显微镜图像并提速4倍。
Generative Image Restoration and Super-Resolution using Physics-Informed Synthetic Data for Scanning Tunneling Microscopy
- 基于36张原始图像生成物理真实合成数据，训练流匹配与扩散模型。
- 重建稀疏采样数据，实现2至4倍图像采集速度提升，保持高保真度。
- 适合加速原子级成像实验，减少探针调节频率，提升实验效率。
扫描隧道显微镜（STM）可实现原子级成像与原子操纵，但受限于探针退化和缓慢的串行数据采集。探针制备过程更复杂，因其常承受大电压，可能改变顶端形状，需反复调节。本文提出一种机器学习方法，用于图像修复与超分辨率，以缓解上述挑战。仅使用36张纯净的Si(001):H实验图像，我们证明物理引导的合成数据生成流程可用于训练多个前沿的流匹配与扩散模型。定量评估显示，模型在CLIP最大均值差异（CMMD）和结构相似性等指标上表现优异，能有效恢复图像，并通过从稀疏采样数据中准确重建，实现2至4倍的图像采集时间缩短。该框架有望显著提升STM实验通量，提供减少探针调节频率的新路径，并增强现有高速STM系统的帧率。
原文摘要 · Abstract (English)
Scanning tunnelling microscopy (STM) enables atomic-resolution imaging and atom manipulation, but its utility is often limited by tip degradation and slow serial data acquisition. Fabrication adds another layer of complexity since the tip is often subjected to large voltages, which may alter the shape of its apex, requiring it to be conditioned. Here, we propose a machine learning (ML) approach for image repair and super-resolution to alleviate both challenges. Using a dataset of only 36 pristine experimental images of Si(001):H, we demonstrate that a physics-informed synthetic data generation pipeline can be used to train several state-of-the-art flow-matching and diffusion models. Quantitative evaluation with metrics such as the CLIP Maximum Mean Discrepancy (CMMD) score and structural similarity demonstrates that our models are able to effectively restore images and offer a two- to fourfold reduction in image acquisition time by accurately reconstructing images from sparsely sampled data. Our framework has the potential to significantly increase STM experimental throughput by offering a route to reducing the frequency of tip-conditioning procedures and to enhancing frame rates in existing high-speed STM systems.
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