Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Journal articles
  4. Large-scale analysis of high-speed atomic force microscopy data sets using adaptive image processing
 
research article

Large-scale analysis of high-speed atomic force microscopy data sets using adaptive image processing

Erickson, Blake W.  
•
Coquoz, Séverine
•
Adams, Jonathan D.
Show more
2012
Beilstein Journal of Nanotechnology

Modern high-speed atomic force microscopes generate significant quantities of data in a short amount of time. Each image in the sequence has to be processed quickly and accurately in order to obtain a true representation of the sample and its changes over time. This paper presents an automated, adaptive algorithm for the required processing of AFM images. The algorithm adaptively corrects for both common one-dimensional distortions as well as the most common two-dimensional distortions. This method uses an iterative thresholded processing algorithm for rapid and accurate separation of background and surface topography. This separation prevents artificial bias from topographic features and ensures the best possible coherence between the different images in a sequence. This method is equally applicable to all channels of AFM data, and can process images in seconds.

  • Details
  • Metrics
Type
research article
DOI
10.3762/bjnano.3.84
Web of Science ID

WOS:000311033900001

Author(s)
Erickson, Blake W.  
Coquoz, Séverine
Adams, Jonathan D.
Burns, Daniel J.
Fantner, Georg E.  
Date Issued

2012

Publisher

Beilstein-Institut

Published in
Beilstein Journal of Nanotechnology
Volume

3

Start page

747

End page

758

Subjects

adaptive algorithm

•

artifact correction

•

atomic force microscopy

•

high-speed atomic force microscope

•

image processing

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LBNI  
Available on Infoscience
January 8, 2013
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/87701
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés