Author:
Schloz Marcel,Müller Johannes,Pekin Thomas C.,Van den Broek Wouter,Madsen Jacob,Susi Toma,Koch Christoph T.
Abstract
AbstractWe present a method that lowers the dose required for an electron ptychographic reconstruction by adaptively scanning the specimen, thereby providing the required spatial information redundancy in the regions of highest importance. The proposed method is built upon a deep learning model that is trained by reinforcement learning, using prior knowledge of the specimen structure from training data sets. We show that using adaptive scanning for electron ptychography outperforms alternative low-dose ptychography experiments in terms of reconstruction resolution and quality.
Funder
Deutsche Forschungsgemeinschaft
HORIZON EUROPE European Research Council
Humboldt-Universität zu Berlin
Publisher
Springer Science and Business Media LLC
Cited by
2 articles.
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