用AI将扫描电镜图像分辨率提升4倍，成像速度加快16倍。
Resolution Enhancement of Scanning Electron Micrographs using Artificial Intelligence
- 基于深度学习的超分辨率算法，实现4倍分辨率提升。
- 成像时间缩短为原来的1/16，且保持高清晰度。
- 适用于罕见事件研究，避免高分辨信息丢失。
扫描电子显微镜（SEM）在揭示微观与纳米尺度结构方面至关重要，但获取高分辨率图像面临扫描时间长、电子束暴露导致样品损伤等问题。本文通过训练并应用一种基于深度学习的超分辨率算法，成功将分辨率提升4倍，使初始成像时间减少至原来的1/16。我们在双相钢和渗碳钢两种材料上进行了基准测试，评估了视觉相似性和相似性指标，结果优于传统插值方法。此外，我们还提出了一种实验流程，可在不损失高分辨信息的前提下研究扫描电镜图像中的稀有事件。
原文摘要 · Abstract (English)
Scanning Electron Microscopy (SEM) is pivotal in revealing intricate micro- and nanoscale features across various research fields. However, obtaining high-resolution SEM images presents challenges, including prolonged scanning durations and potential sample degradation due to extended electron beam exposure. This paper addresses these challenges by training and applying a deep learning based super-resolution algorithm. We show that the chosen algorithm is capable of increasing the resolution by a factor of 4, thereby reducing the initial imaging time by a factor of 16. We benchmark our method in terms of visual similarity and similarity metrics on two different materials, a dual-phase steel and a case-hardening steel, improving over standard interpolation methods. Additionally, we introduce an experimental pipeline for the study of rare events in scanning electron micrographs, without losing high-resolution information.
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