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Comparative study of deep learning algorithms for atomic force microscopy image denoising

Authors
Jung, HoichanHan, GiwoongJung, Seong JunHan, Sung Won
Issue Date
10월-2022
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Keywords
Atomic force microscopy image; Deep neural network; Image denoising; Image restoration
Citation
MICRON, v.161
Indexed
SCIE
SCOPUS
Journal Title
MICRON
Volume
161
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/144074
DOI
10.1016/j.micron.2022.103332
ISSN
0968-4328
Abstract
Atomic force microscopy (AFM) enables direct visualisation of surface topography at the nanoscale. However, post-processing is generally required to obtain accurate, precise, and reliable AFM images owing to the presence of image artefacts. In this study, we compared and analysed state-of-the-art deep learning models, namely MPRNet, HINet, Uformer, and Restormer, with respect to denoising AFM images containing four types of noise. Specifically, these algorithms' denoising performance and inference time on AFM images were compared with those of conventional methods and previous studies. Through a comparative analysis, we found that the most efficient and the most effective models were Restormer and HINet, respectively. The code, models, and data used in this work are available at https://github.com/hoichanjung/AFM_Image_Denoising.
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공과대학 (산업경영공학부)
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