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Project

IBAS-STEM

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.

Sponsors

FEI Direktauftrag

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Publications about the project

Feature Adaptive Sampling for Scanning Electron Microscopy

Tim Dahmen; Michael Engstler; Christoph Pauly; Patrick Trampert; Nils de Jonge; Frank Mücklich; 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.

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Feature Adaptive Sampling for Scanning Electron Microscopy

Tim Dahmen; Michael Engstler; Christoph Pauly; Patrick Trampert; Niels de Jonge; Frank Mücklich; Philipp Slusallek

In: Scientific Reports (Sci Rep), Vol. 6, Page 25350, Nature Publishing Group, 5/2016.

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