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General Resolution Enhancement Method in Atomic Force Microscopy Using Deep Learning
Author(s) -
Liu Yue,
Sun Qiaomei,
Lu Wanheng,
Wang Hongli,
Sun Yao,
Wang Zhongting,
Lu Xin,
Zeng Kaiyang
Publication year - 2019
Publication title -
advanced theory and simulations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.068
H-Index - 17
ISSN - 2513-0390
DOI - 10.1002/adts.201800137
Subject(s) - atomic force microscopy , resolution (logic) , image resolution , convolution (computer science) , materials science , artificial intelligence , image processing , high resolution , microscopy , image (mathematics) , convolutional neural network , optics , computer science , computer vision , artificial neural network , nanotechnology , physics , remote sensing , geology
Here, a resolution enhancement method is developed for post‐processing images from atomic force microscopy (AFM). This method is based on deep learning neural networks in the AFM topography measurements. In this study, a very deep convolution neural network is developed to derive a high‐resolution topography image from a low‐resolution topography image. The AFM measured images from various materials are tested in this study. The derived high‐resolution AFM images are comparable with the experimental measured high‐resolution images measured at the same locations. The results suggest that this method can be developed as a general post‐processing method for AFM image analysis.