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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 |
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Publication date | 2014 |
DOIs | |
Publication status | Published - 2014 |
Keywords
- Compressed Sensing
- Atomic Force Microscopy
- Image Processing
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Dive into the research topics of 'Magni'. Together they form a unique fingerprint.Projects
- 1 Finished
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FastAFM: Enabling Fast Image Acquisition for Atomic Force Microscopy using Compressed Sensing
Larsen, T., Østergaard, J., Jensen, T., Arildsen, T., Oxvig, C. S. & Pedersen, P. S.
01/09/2013 → 31/08/2016
Project: Research
Press/Media
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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
1 item of Media coverage
Press/Media: Press / Media