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• W3150666581 abstract "Abstract 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 ." @default.
• W3150666581 created "2021-04-13" @default.
• W3150666581 creator A5001453396 @default.
• W3150666581 date "2021-07-19" @default.
• W3150666581 modified "2023-09-26" @default.
• W3150666581 title "Adaptive partial scanning transmission electron microscopy with reinforcement learning" @default.
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• W3150666581 doi "https://doi.org/10.1088/2632-2153/abf5b6" @default.
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