论文标题
对数据驱动的自适应扫描的深度强化学习
Deep Reinforcement Learning for Data-Driven Adaptive Scanning in Ptychography
论文作者
论文摘要
我们提出了一种方法，该方法可以通过自适应扫描样品来降低Ptychographic重建所需的剂量，从而在最重要的区域提供所需的空间信息冗余。所提出的方法是基于通过培训数据集对标本结构的先验知识进行的深入学习模型（RL）培训的。我们表明，使用自适应扫描的等效低剂量实验在重建分辨率方面超过常规的Ptychography实验。
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.
