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The paper introduces a spatial-frequency learning framework for enhancing Scanning Transmission Electron Microscopy (STEM) images. It employs a spatial-frequency fusion network that separately enhances features in the spatial and frequency domains before fusing them, leveraging periodic atomic arrangements in the frequency domain and local atomic details in the spatial domain. Additionally, the authors present a STEM noise calibration method to synthesize realistic images, creating a diverse dataset that improves performance on synthetic and real advanced material images, including better atomic localization accuracy.
arXiv:2606. 09419v1 Announce Type: cross Abstract: Artificial intelligence is rapidly advancing materials characterization, yet most applications in electron microscopy rely solely on image contrast, overlooking the chemical and experimental context that shapes image formation.
arXiv:2609.36639v1 Announce Type: new Abstract: Scanning Tunneling Microscopy (STM) is a widely used tool for characterizing surfaces of materials at the atomic scale, playing a crucial role in disco...
arXiv:2606.24817v3 Announce Type: replace Abstract: Advanced semiconductor nodes drastically increased demand for Transmission Electron Microscopy (TEM), yet destructive sample preparation, slow imag...
arXiv:2606. 29592v1 Announce Type: new Abstract: A central premise of autonomous scientific imaging is that smarter navigation, whether Bayesian, RL-based, or otherwise adaptive, is the principal lever for sample-efficient acquisition.
A central premise of autonomous scientific imaging is that smarter navigation, whether Bayesian, RL-based, or otherwise adaptive, is the principal lever for sample-efficient acquisition. We present evidence to the contrary in scanning transmission electron microscopy (STEM), an atomic-resolution imaging modality whose every measurement deposits damaging electron dose.