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Atomic Force Microscopy

Atomic Force Microscopy

Atomic force microscopy (AFM) is a type of scanning probe microscopy that can analyze topographic details of various specimens like ceramics, glass, polymers, and biological samples. AFM offers over 1000 times more resolution than the optical imaging system. Images generated from AFM are three-dimensional surface profiles, offering an advantage over the flat, two-dimensional images from other imaging techniques.
The AFM Probe
The probe is regarded as the heart of any AFM setup and comprises the...

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Updated: Sep 1, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

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

Hoichan Jung¹, Giwoong Han¹, Seong Jun Jung²

¹Department of Industrial and Management Engineering, Korea University, Seoul 02841, the Republic of Korea.

Micron (Oxford, England : 1993)

|August 11, 2022

Summary

This summary is machine-generated.

Deep learning models like Restormer and HINet effectively denoise atomic force microscopy (AFM) images, improving nanoscale surface topography visualization. These advanced algorithms offer superior performance compared to conventional methods for removing image artifacts.

Keywords:

Atomic force microscopy image Deep neural network Image denoising Image restoration

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Published on: July 3, 2021

Area of Science:

Materials Science
Nanotechnology
Computational Imaging

Background:

Atomic Force Microscopy (AFM) is crucial for nanoscale surface topography visualization.
AFM images often contain artifacts requiring post-processing for accuracy and reliability.
Deep learning offers potential for advanced image artifact removal.

Purpose of the Study:

To compare the performance of state-of-the-art deep learning models for denoising AFM images.
To analyze the denoising effectiveness and inference time of MPRNet, HINet, Uformer, and Restormer.
To benchmark deep learning against conventional denoising methods.

Main Methods:

Applied four deep learning models (MPRNet, HINet, Uformer, Restormer) to AFM images with four distinct noise types.
Evaluated denoising performance and inference speed.
Compared results with conventional techniques and prior research.

Main Results:

Restormer demonstrated the highest efficiency in terms of inference time.
HINet exhibited the most effective denoising performance.
Both models significantly outperformed conventional methods.

Conclusions:

Deep learning models, particularly Restormer and HINet, offer significant improvements in AFM image quality.
These models provide accurate and reliable nanoscale surface topography data.
The study provides open-source code, models, and data for AFM image denoising research.