Abstract
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.
Highlights
Atomic force microscopes (AFMs) are a useful tool for investigating nanoscale surfaces
We have developed an automated, adaptive image-processing algorithm for high-speed AFM image sequences
This is achieved by identifying the background region and determining line-by-line offsets using an iterative process
Summary
Atomic force microscopes (AFMs) are a useful tool for investigating nanoscale surfaces. ; the geometric distortions will always broaden the distribution of observed heights relative to the true sample topography This means that as long as the processing steps only affect (flattening is only performed on truly flat regions, and not real topography), the minimization of will improve the image. Inherent 2-D distortions because they will destroy interline relationships in the data, and can generate false artifacts between lines [13,31,37], see Figure 2B and Figure 2F which show the results of 1-D second-order polynomial removal This line-byline polynomial subtraction generates many artifacts in the data, for example the surface surrounding the pits appears raised along the fast scan axis, and continuous levels in Figure 2F have offsets from line to line and are not perpendicular to the image plane. Once these quantities are known, subtracting the line offsets and correcting the 2-D distortions can be performed with only two imageprocessing steps on the raw data
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
