Reconstruction Algorithms in Undersampled AFM Imaging

We just had a new paper published in IEEE Journal of Selected Topics in Signal Processing in the February, 2016 issue. This is an attempt to create an overview of some of the basic possibilities in sparse image reconstruction / inverse problems that can be used to reconstruct images from undersampled measurements in atomic force microscopy.

Abstract

This paper provides a study of spatial undersampling in atomic force microscopy (AFM) imaging followed by different image reconstruction techniques based on sparse approximation as well as interpolation. The main reasons for using undersampling is that it reduces the path length and thereby the scanning time as well as the amount of interaction between the AFM probe and the specimen. It can easily be applied on conventional AFM hardware. Due to undersampling, it is necessary to subsequently process the acquired image in order to reconstruct an approximation of the image. Based on real AFM cell images, our simulations reveal that using a simple raster scanning pattern in combination with conventional image interpolation performs very well. Moreover, this combination enables a reduction by a factor 10 of the scanning time while retaining an average reconstruction quality around 36 dB PSNR on the tested cell images.

Download

• The paper has been deposited open access here: Aalborg University Research Portal
• The IEEE version of the paper is available here, DOI: 10.1109/JSTSP.2015.2500363.
• The accompanying Python source code can be downloaded here, DOI: 10.5281/zenodo.32959
• The simulation results behind the paper can be downloaded here, DOI: 10.5281/zenodo.32958
• The experiments in the paper also make use of the Magni package: https://github.com/SIP-AAU/Magni/ (issues and pull requests very welcome!)