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arXiv Computer Vision By Hesong Li, Ying Fu, Ziqi Wu, Ruiwen Shao, Tao Zhang

Spatial-Frequency Learning for Scanning Transmission Electron Microscopy Image Enhancement

Read the original on arXiv Computer Vision →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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