[2012.09093] TEMImageNet and AtomSegNet Deep Learning Training Library and Models for High-Precision Atom Segmentation, Localization, Denoising, and Super-resolution Processing of Atom-Resolution Scan

Atom segmentation and localization, noise reduction and super-resolution processing of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic columns when there is large thickness variation in a recorded image. Herein, we report the development of a training library and a deep learning method that can perform robust and precise atom segmentation, localization, denoising, and super-resolution processing of experimental images. Despite using simulated images as training datasets, the deep-learning model can self-adapt to e

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@tjmlab TEMImageNet and AtomSegNet Deep Learning Training Library and Models for High-Precision Atom Segmentation, ~ t.co/y2KtGpuiDm STEMの超高分解能イメージングを可能にする深層学習手法の提案、時代を感じる。

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