Magni

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

doi:10.5278/VBN/MISC/Magni

Magni

Christian Schou Oxvig (Developer), Patrick Steffen Pedersen (Developer), Jan Østergaard, Thomas Arildsen, Tobias Lindstrøm Jensen, Torben Larsen

Department of Electronic Systems

Research output: Non-textual form › Computer programme › Research › peer-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 language	English
Publication date	2014
DOIs	https://doi.org/10.5278/VBN/MISC/Magni
Publication status	Published - 2014

Keywords

Compressed Sensing
Atomic Force Microscopy
Image Processing

Access to Document

10.5278/VBN/MISC/Magni

Magni-1.7.0Other version, 17.2 MBLicence: Other
Magni_1.6.0Other version, 17.2 MBLicence: Other
Magni-1.5.0Other version, 16.5 MBLicence: Other
Magni-1.4.0Other version, 16.4 MBLicence: Other
Magni-1.3.0Other version, 15.3 MBLicence: Other
Magni-1.2.0Other version, 14 MBLicence: Other
Magni-1.1.0Other version, 14 MBLicence: Other
Magni-1.0.0Other version, 12.9 MBLicence: Other

http://magni.readthedocs.io/Licence: Other
https://github.com/SIP-AAU/MagniLicence: Other

AUB Link

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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",

}

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 -

Magni

Abstract

Keywords

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FastAFM: Enabling Fast Image Acquisition for Atomic Force Microscopy using Compressed Sensing

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

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Magni

Abstract

Keywords

Access to Document

AUB Link

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Projects

FastAFM: Enabling Fast Image Acquisition for Atomic Force Microscopy using Compressed Sensing

Press/Media

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

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