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Fully-automatic defects classification and restoration for STM images.
Micron ( IF 2.4 ) Pub Date : 2019-12-05 , DOI: 10.1016/j.micron.2019.102798
Xian-Guang Fan 1 , Yi Wu 2 , Yu-Liang Zhi 2 , Hong Xia 2 , Xin Wang 1
Affiliation  

The Scanning tunneling microscope (STM) is a micro instrument designed for surface morphology with nanometer precision. The restoration of the STM image defects usually needs human judgements and manual positioning because of the diversity of the morphology and the randomness of the defects. This paper provides a new fully-automatic method that combines deep convolutional neural classification network and unique restoration algorithms corresponding to different defects. Aimed at automatically processing compound defects in STM images, the method first predicts what kinds of defects a raw STM image has by a series of parallel binary classification networks, and then decides the process order according to the predicted labels, and finally restores the defects by corresponding global restoration algorithms in order. Experiment results prove the provided method can restore the STM images by self-judging, self-positioning, self-processing without any manual intervention.



中文翻译:

STM图像的全自动缺陷分类和恢复。

扫描隧道显微镜(STM)是一种用于纳米表面精度的微仪器。由于形态的多样性和缺陷的随机性,通常需要人工判断和手动定位STM图像缺陷的修复。本文提供了一种新的全自动方法,该方法结合了深度卷积神经分类网络和对应于不同缺陷的独特还原算法。为了自动处理STM图像中的复合缺陷,该方法首先通过一系列并行的二进制分类网络预测原始STM图像具有哪些缺陷,然后根据预测的标签确定处理顺序,最后通过以下步骤恢复缺陷:相应的全局恢复算法。

更新日期:2019-12-05
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