Exemplar-Based Inpainting Based on Dictionary Learning for Sparse Scanning Electron Microscopy

Patrick Trampert, Sabine Schlabach, Tim Dahmen, Philipp Slusallek

In: Microscopy and Microanalysis 24 S1 Seiten 700-701 Cambridge University Press 8/2018.


High-throughput scanning electron microscopy (SEM) aims to reduce dose for sensitive specimens as well as reducing acquisition times to be able to acquire large volumes in a meaningful time. Sparse sampling is one key to make such acquisitions possible. We propose a new reconstruction technique for such sparsely sampled SEM data, which is based on exemplar-based inpainting known from image processing.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence