Magni

Research output: Non-textual formComputer programmeResearchpeer-review

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Abstract

Magni is a Python package which provides functionality for increasing the speed of image acquisition using Atomic Force Microscopy (AFM). The image acquisition algorithms of Magni are based on the Compressed Sensing (CS) signal acquisition paradigm and include both sensing and reconstruction. The sensing part of the acquisition generates sensed data from regular images possibly acquired using AFM. This is done by AFM hardware simulation. The reconstruction part of the acquisition reconstructs images from sensed data. This is done by CS reconstruction using well-known CS reconstruction algorithms modified for the purpose. The Python implementation of the above functionality uses the standard library, a number of third-party libraries, and additional utility functionality designed and implemented specifically for Magni.
Original languageEnglish
Publication date2014
DOIs
Publication statusPublished - 2014

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Compressed sensing
Image acquisition
Atomic force microscopy
Hardware

Keywords

  • Compressed Sensing
  • Atomic Force Microscopy
  • Image Processing

Cite this

@misc{194fc19379134b8885d625570aa43ae1,
title = "Magni",
abstract = "Magni is a Python package which provides functionality for increasing the speed of image acquisition using Atomic Force Microscopy (AFM). The image acquisition algorithms of Magni are based on the Compressed Sensing (CS) signal acquisition paradigm and include both sensing and reconstruction. The sensing part of the acquisition generates sensed data from regular images possibly acquired using AFM. This is done by AFM hardware simulation. The reconstruction part of the acquisition reconstructs images from sensed data. This is done by CS reconstruction using well-known CS reconstruction algorithms modified for the purpose. The Python implementation of the above functionality uses the standard library, a number of third-party libraries, and additional utility functionality designed and implemented specifically for Magni.",
keywords = "Compressed Sensing, Atomic Force Microscopy, Image Processing",
author = "Oxvig, {Christian Schou} and Pedersen, {Patrick Steffen} and Jan {\O}stergaard and Thomas Arildsen and Jensen, {Tobias Lindstr{\o}m} and Torben Larsen",
year = "2014",
doi = "10.5278/VBN/MISC/Magni",
language = "English",

}

Magni. Oxvig, Christian Schou (Developer); Pedersen, Patrick Steffen (Developer); Østergaard, Jan (Author); Arildsen, Thomas (Author); Jensen, Tobias Lindstrøm (Author); Larsen, Torben (Author). 2014.

Research output: Non-textual formComputer programmeResearchpeer-review

TY - COMP

T1 - Magni

AU - Østergaard, Jan

AU - Arildsen, Thomas

AU - Jensen, Tobias Lindstrøm

AU - Larsen, Torben

A2 - Oxvig, Christian Schou

A2 - Pedersen, Patrick Steffen

PY - 2014

Y1 - 2014

N2 - Magni is a Python package which provides functionality for increasing the speed of image acquisition using Atomic Force Microscopy (AFM). The image acquisition algorithms of Magni are based on the Compressed Sensing (CS) signal acquisition paradigm and include both sensing and reconstruction. The sensing part of the acquisition generates sensed data from regular images possibly acquired using AFM. This is done by AFM hardware simulation. The reconstruction part of the acquisition reconstructs images from sensed data. This is done by CS reconstruction using well-known CS reconstruction algorithms modified for the purpose. The Python implementation of the above functionality uses the standard library, a number of third-party libraries, and additional utility functionality designed and implemented specifically for Magni.

AB - Magni is a Python package which provides functionality for increasing the speed of image acquisition using Atomic Force Microscopy (AFM). The image acquisition algorithms of Magni are based on the Compressed Sensing (CS) signal acquisition paradigm and include both sensing and reconstruction. The sensing part of the acquisition generates sensed data from regular images possibly acquired using AFM. This is done by AFM hardware simulation. The reconstruction part of the acquisition reconstructs images from sensed data. This is done by CS reconstruction using well-known CS reconstruction algorithms modified for the purpose. The Python implementation of the above functionality uses the standard library, a number of third-party libraries, and additional utility functionality designed and implemented specifically for Magni.

KW - Compressed Sensing

KW - Atomic Force Microscopy

KW - Image Processing

U2 - 10.5278/VBN/MISC/Magni

DO - 10.5278/VBN/MISC/Magni

M3 - Computer programme

ER -