Projects per year
Abstract
Pseudo-random numbers are often used for generating incoherent uniformly distributed sample distributions. However randomness is a sufficient -- not necessary -- condition to ensure incoherence. If one wants to reconstruct an image from few samples, choosing a globally optimized set of evenly distributed points could capture the visual content more efficiently. This work compares classical random sampling with a simple construction based on properties of the fractional Golden ratio sequence and the Hilbert space filling curve. Images are then reconstructed using a total variation prior. Results show improvements in terms of peak signal to noise ratio over pseudo-random sampling.
Original language | English |
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Title of host publication | iTWIST'14, international - Traveling Workshop on Interactions between Sparse models and Technology |
Editors | Laurent Jacques |
Pages | 57-58 |
Publication status | Published - 2014 |
Event | iTWIST'14 international Traveling Workshop on Interactions between Sparse models and Technology - Namur, Belgium Duration: 27 Aug 2014 → 29 Aug 2014 |
Conference
Conference | iTWIST'14 international Traveling Workshop on Interactions between Sparse models and Technology |
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Country/Territory | Belgium |
City | Namur |
Period | 27/08/14 → 29/08/14 |
Bibliographical note
Laurent JacquesKeywords
- Compressed sensing
- Total variation
Fingerprint
Dive into the research topics of 'Total Variation Reconstruction From Quasi-Random Samples'. Together they form a unique fingerprint.Projects
- 2 Finished
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EU465: INTERFERE: Sparse Signal Coding for Interference-based Imaging Modalities
1/06/14 → 31/05/19
Project: Fundamental
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SRP11: Strategic Research Programme: Processing of large scale multi-dimensional, multi-spectral, multi-sensorial and distributed data (M³D²)
Schelkens, P., Deligiannis, N., Jansen, B., Kuijk, M., Munteanu, A., Sahli, H., Steenhaut, K., Stiens, J., Schelkens, P., Cornelis, J. P., Kuijk, M., Munteanu, A., Sahli, H., Stiens, J. & Vounckx, R.
1/11/12 → 31/12/23
Project: Fundamental