Image-Based Adaptive Sampling for SEM and STEM Imaging

  • Duration:

In this project, we will develop sparse sampling strategies, also called compressed sensing (CS), to increase in the throughput and reduce the required electron dose of three dimensional (3D) Scanning Electron Microscopy (SEM) imaging platforms, particularly in the field of life sciences. Hereby, a solution will be investigated for using prior image knowledge and CS algorithms to reduce the overall samples required for reconstructing high-resolution 3D datasets. This way, electron dose can be spent more effectively compared to a sampling scheme based on a uniform grid.


FEI Direktauftrag

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Contact Person
Prof. Dr.-Ing. Philipp Slusallek
Prof. Dr.-Ing. Philipp Slusallek


Publications about the project

Michael Engstler, Christoph Pauly, Nils de Jonge, Frank Mücklich, Tim Dahmen, Philipp Slusallek

In: 12th European Congress for Stereology and Image Analysis 2017. European Congress for Stereology and Image Analysis (ECSIA-17) 12th September 11-14 Kaiserslautern Germany ISSIA 2017.

To the publication

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz