TY - JOUR
T1 - Comparative study of deep learning algorithms for atomic force microscopy image denoising
AU - Jung, Hoichan
AU - Han, Giwoong
AU - Jung, Seong Jun
AU - Han, Sung Won
N1 - Funding Information:
This research was supported by Brain Korea 21 FOUR . This research was also supported by Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) ( P0008691 , The Competency Development Program for Industry Specialist).
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/10
Y1 - 2022/10
N2 - 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.
AB - 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.
KW - Atomic force microscopy image
KW - Deep neural network
KW - Image denoising
KW - Image restoration
UR - http://www.scopus.com/inward/record.url?scp=85136107320&partnerID=8YFLogxK
U2 - 10.1016/j.micron.2022.103332
DO - 10.1016/j.micron.2022.103332
M3 - Article
C2 - 35952420
AN - SCOPUS:85136107320
SN - 0968-4328
VL - 161
JO - Micron and Microscopica Acta
JF - Micron and Microscopica Acta
M1 - 103332
ER -