论文标题
通过增强学习的自适应部分扫描传输电子显微镜
Adaptive Partial Scanning Transmission Electron Microscopy with Reinforcement Learning
论文作者
论文摘要
压缩传感可以减少扫描透射电子显微镜电子剂量和扫描时间，而信息损失最小。传统上，在压缩感测样品中使用的稀疏扫描一组静态的探测位置。但是，由于静态扫描是可能的动态扫描的一部分，预计适应标本的动态扫描将能够匹配或超过静态扫描的性能。因此，我们为连续的稀疏扫描系统提出了一个原型，该系统将扫描路径划分为扫描时。基于先前观察到的扫描段，通过复发性神经网络选择扫描段的采样方向。复发性神经网络通过强化学习训练，可以与完成稀疏扫描的前馈卷积神经网络合作。本文介绍了我们的学习政策，实验和示例部分扫描，并讨论了未来的研究方向。源代码，预算模型和培训数据可在https://github.com/jeffrey-ede/adaptive-scans上公开访问
Compressed sensing can decrease scanning transmission electron microscopy electron dose and scan time with minimal information loss. Traditionally, sparse scans used in compressed sensing sample a static set of probing locations. However, dynamic scans that adapt to specimens are expected to be able to match or surpass the performance of static scans as static scans are a subset of possible dynamic scans. Thus, we present a prototype for a contiguous sparse scan system that piecewise adapts scan paths to specimens as they are scanned. Sampling directions for scan segments are chosen by a recurrent neural network based on previously observed scan segments. The recurrent neural network is trained by reinforcement learning to cooperate with a feedforward convolutional neural network that completes the sparse scans. This paper presents our learning policy, experiments, and example partial scans, and discusses future research directions. Source code, pretrained models, and training data is openly accessible at https://github.com/Jeffrey-Ede/adaptive-scans
