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Atomic Force Microscopy01:08

Atomic Force Microscopy

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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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Related Experiment Video

Updated: Mar 25, 2026

Contact Mode Atomic Force Microscopy as a Rapid Technique for Morphological Observation and Bacterial Cell Damage Analysis
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Double-Tip Artefact Removal from Atomic Force Microscopy Images.

Yun-Feng Wang, Jason Kilpatrick, Suzanne Jarvis

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |February 26, 2016
    PubMed
    Summary

    This study introduces a novel Bayesian deblurring technique to reconstruct accurate Atomic Force Microscope (AFM) images. The method effectively removes imaging artifacts like the

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    Area of Science:

    • Nanotechnology
    • Surface Science
    • Microscopy

    Background:

    • Atomic Force Microscopy (AFM) enables nanoscale interface measurements.
    • Tip imperfections cause imaging artifacts like blurring and object repetition.
    • Discarding data or replacing the probe is often infeasible for artifact removal.

    Purpose of the Study:

    • To develop a novel deblurring technique for Atomic Force Microscope (AFM) imaging.
    • To achieve reliable surface topography estimation without prior knowledge of tip geometry (blind reconstruction).
    • To specifically address and remove the 'double-tip' artifact in AFM images.

    Main Methods:

    • Application of a Bayesian deblurring framework.
    • Leveraging techniques from natural image deblurring.
    • Focusing on blind reconstruction to overcome unknown tip geometry.

    Main Results:

    • Successful removal of the 'double-tip' artifact from high-resolution AFM images.
    • Preservation of feature resolution in deblurred images.
    • Demonstration of reliable surface topography estimation.

    Conclusions:

    • The novel Bayesian deblurring technique effectively reconstructs AFM images.
    • This method provides a feasible solution for artifact removal when probe replacement is not an option.
    • The technique enhances the reliability and accuracy of AFM surface topography measurements.