Magni: A Python Package for Compressive Sampling and Reconstruction of Atomic Force Microscopy Images

Christian Schou Oxvig; Patrick Steffen Pedersen; Thomas Arildsen; Jan Østergaard; Torben Larsen

doi:10.5334/jors.bk

Magni: A Python Package for Compressive Sampling and Reconstruction of Atomic Force Microscopy Images

Journal of Open Research Software

Volume 2 (2014): Issue 1

By: Christian Schou Oxvig, Patrick Steffen Pedersen, Thomas Arildsen, Jan Østergaard and Torben Larsen

Open Access

|Oct 2014

Abstract

Magni is an open source Python package that embraces compressed sensing and Atomic Force Microscopy (AFM) imaging techniques. It provides AFM-specific functionality for undersampling and reconstructing images from AFM equipment and thereby accelerating the acquisition of AFM images. Magni also provides researchers in compressed sensing with a selection of algorithms for reconstructing undersampled general images, and offers a consistent and rigorous way to efficiently evaluate the researchers own developed reconstruction algorithms in terms of phase transitions. The package also serves as a convenient platform for researchers in compressed sensing aiming at obtaining a high degree of reproducibility of their research.

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Articles in this issue

DOI: https://doi.org/10.5334/jors.bk | Journal eISSN: 2049-9647

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Language: English

Published on: Oct 7, 2014

Published by: Ubiquity Press

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

omic Force Microscopy,

Compressive Sensing,

Python,

Image Reconstruction,

Reproducible Research

© 2014 Christian Schou Oxvig, Patrick Steffen Pedersen, Thomas Arildsen, Jan Østergaard, Torben Larsen, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 2 (2014): Issue 1