English(EN) Spatial-Frequency Learning for Scanning Transmission Electron Microscopy Image Enhancement

新框架利用空频学习增强STEM图像

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了一种新的空频学习框架，用于增强扫描透射电子显微镜（STEM）捕获的图像。该方法利用了空间频率融合网络，该网络利用了频域中的原子排列并恢复了空间细节。该框架还包括一种新颖的STEM噪声校准技术，以创建更真实的训练数据，从而在提高合成和真实世界先进材料图像的原子定位精度方面取得了最先进的性能。 AI

影响这种新方法可以提高科学成像的准确性和分辨率，有助于材料科学的发现。

排序理由该集群包含一篇详细介绍科学背景下图像增强新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Hesong Li, Ying Fu, Ziqi Wu, Ruiwen Shao, Tao Zhang · 2026-10-28 04:00

用于扫描透射电子显微镜图像增强的空频学习

arXiv:2610.36487v1 Announce Type: new Abstract: Scanning Transmission Electron Microscopy (STEM) enables the observation of atomic arrangements at sub-angstrom resolution and plays a key role in the discovery and characterization of advanced materials. Raw STEM images are often n…

本文如何被排名

报道来源 [1]

用于扫描透射电子显微镜图像增强的空频学习

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