SPARSE -- Efficient High-Resolution SEM Imaging of Rare Microstructural Features Across Large Areas by Selective Rescanning
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Le résumé fourni par la source
SPARSE is an open-source framework for automated panoramic imaging with scanning electron microscopes (SEM). Resolving rare microstructural features across a large sample area is prohibitively slow when the whole area is acquired at full imaging quality. SPARSE instead acquires the sample first with a fast overview pass (a shorter pixel dwell time, a lower resolution, or a combination of both) automatically locates the points of interest, and rescans only those locations at full imaging quality, concentrating acquisition time where it is actually needed. The framework acquires a boustrophedon grid of tiles; each tile is subdivided into four partitions that are fast-scanned, analysed for points of interest, and selectively rescanned. Fast acquisition and point-of-interest analysis run concurrently in two processes, so detection never blocks the microscope. SPARSE is instrument- and method-agnostic. Both the microscope control interface and the detection method are pluggable through documented base classes: this release ships an implementation for Tescan SharkSEM instruments and a dark-particle detector based on intensity thresholding and connected-component analysis, and either can be replaced to support other hardware or detection algorithms. This is version 1.0.0 of the software, accompanying the preprint: Reclik, T., Gerlach, J., Wollenweber, M. A., Korkolis, Y. P., Korte-Kerzel, S., & Kerzel, U. (2026). SPARSE – Efficient High-Resolution SEM Imaging of Rare Microstructural Features Across Large Areas by Selective Rescanning. arXiv:2604.24769. https://arxiv.org/abs/2604.24769
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RWTH Aachen University pays non établi dans la noticeUniversité ou école supérieure
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TU Dortmund University pays non établi dans la noticeUniversité ou école supérieure
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RWTH Business School pays non établi dans la noticeInstitution
RWTH Aachen University, TU Dortmund University et RWTH Business School.
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