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 (RNN) based on previously observed scan segments. The RNN 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.

压缩传感可以以最小的信息损失减少扫描透射电子显微镜的电子剂量和扫描时间。传统上，压缩感知中使用的稀疏扫描对一组静态探测位置进行采样。然而，由于静态扫描是可能的动态扫描的一个子集，因此适应样本的动态扫描预计能够匹配或超过静态扫描的性能。因此，我们提出了一个连续稀疏扫描系统的原型，该系统在扫描样本时分段适应样本的扫描路径。扫描段的采样方向由循环神经网络 (RNN) 根据先前观察到的扫描段选择。RNN 通过强化学习进行训练，以配合完成稀疏扫描的前馈卷积神经网络。本文介绍了我们的学习策略，实验和示例部分扫描，并讨论未来的研究方向。源代码、预训练模型和训练数据可在 https://github.com/Jeffrey-Ede/adaptive-scans 上公开访问。