Magni: En Python-pakke til komprimeret sampling og rekonstruktion af billeder fra atomar kraft-mikroskopi

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Resumé

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.
Bidragets oversatte titelMagni: En Python-pakke til komprimeret sampling og rekonstruktion af billeder fra atomar kraft-mikroskopi
OriginalsprogEngelsk
TidsskriftJournal of Open Research Software
Vol/bind2
Udgave nummer1
ISSN2049-9647
DOI
StatusUdgivet - 7 okt. 2014

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    title = "Magni: A Python Package for Compressive Sampling and Reconstruction of Atomic Force Microscopy Images",
    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.",
    keywords = "Atomic Force Microscopy, Compressive sensing, Python, Image Reconstruction, Reproducible Research",
    author = "Oxvig, {Christian Schou} and Pedersen, {Patrick Steffen} and Thomas Arildsen and Jan {\O}stergaard and Torben Larsen",
    year = "2014",
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    Magni: A Python Package for Compressive Sampling and Reconstruction of Atomic Force Microscopy Images. / Oxvig, Christian Schou; Pedersen, Patrick Steffen; Arildsen, Thomas; Østergaard, Jan; Larsen, Torben.

    I: Journal of Open Research Software, Bind 2, Nr. 1, 07.10.2014.

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

    TY - JOUR

    T1 - Magni: A Python Package for Compressive Sampling and Reconstruction of Atomic Force Microscopy Images

    AU - Oxvig, Christian Schou

    AU - Pedersen, Patrick Steffen

    AU - Arildsen, Thomas

    AU - Østergaard, Jan

    AU - Larsen, Torben

    PY - 2014/10/7

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    N2 - 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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    KW - Compressive sensing

    KW - Python

    KW - Image Reconstruction

    KW - Reproducible Research

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