Abstract
Compressed sensing (CS) is a valuable technique for reconstructing measurements in numerous domains. CS has not yet gained widespread adoption in scanning tunneling microscopy (STM), despite potentially offering the advantages of lower acquisition time and enhanced tolerance to noise. Here we applied a simple CS framework, using a weighted iterative thresholding algorithm for CS reconstruction, to representative high-resolution STM images of superconducting surfaces and adsorbed molecules. We calculated reconstruction diagrams for a range of scanning patterns, sampling densities, and noise intensities, evaluating reconstruction quality for the whole image and chosen defects. Overall, we find that typical STM images can be satisfactorily reconstructed down to 30% sampling—already a strong improvement. We furthermore outline limitations of this method, such as sampling pattern artifacts, which become particularly pronounced for images with intrinsic long-range disorder, and propose ways to mitigate some of them. Finally, we investigate compressibility of STM images as a measure of intrinsic noise in the image and a precursor to CS reconstruction, enabling a priori estimation of the effectiveness of CS reconstruction with minimal computational cost.
| Original language | English |
|---|---|
| Article number | 043040 |
| Journal | Physical Review Research |
| Volume | 3 |
| Issue number | 4 |
| DOIs | |
| State | Published - Dec 2021 |
Funding
We gratefully acknowledge Seokmin Jeon and Simon Kelly for their help with sample preparation for STM experiments with adsorbed molecules. Data analysis and interpretation was sponsored by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division. Experimental data was acquired at the Center for Nanophase Materials Sciences, which is a DOE Office of Science User Facility. Student (B.E.L., A.F.G.) research support was provided by the DOE Science Undergraduate Laboratory Internships (SULI) program. This research used resources of the Compute and Data Environment for Science (CADES) at Oak Ridge National Laboratory, and the paper has been coauthored by employees of UT-Battelle, LLC, which are supported by the U.S. Department of Energy, Office of Science under Contract No. DE-AC05-00OR22725. The U.S. Government retains and the publisher, by accepting the paper for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this paper, or allow others to do so, for U.S. Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan .