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Deep Reinforcement Learning for Data-Driven Adaptive Scanning in Ptychography

2022-03-29 10:25:02
Marcel Schloz, Johannes Müller, Thomas C. Pekin, Wouter Van den Broek, Christoph T. Koch
arXiv_CV
           
Abstract

We present a method that lowers the dose required for a 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 (RL), using prior knowledge of the specimen structure from training data sets. We show that equivalent low-dose experiments using adaptive scanning outperform conventional ptychography experiments in terms of reconstruction resolution.

Abstract (translated)

URL

https://arxiv.org/abs/2203.15413

PDF

https://arxiv.org/pdf/2203.15413.pdf

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