Magni

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

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

Keywords

  • Compressed Sensing
  • Atomic Force Microscopy
  • Image Processing

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

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

    08/10/2014

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    Press/Media: Press / Media

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