Magni

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Resumé

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.
OriginalsprogEngelsk
Publikationsdato2014
DOI
StatusUdgivet - 2014

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Compressed sensing
Image acquisition
Atomic force microscopy
Hardware

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    @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 (Udvikler); Pedersen, Patrick Steffen (Udvikler); Østergaard, Jan (Forfatter); Arildsen, Thomas (Forfatter); Jensen, Tobias Lindstrøm (Forfatter); Larsen, Torben (Forfatter). 2014.

    Publikation: Bidrag der ikke har en tekstformSoftwareprogramForskningpeer 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 -