Projects per year
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.
|Title of host publication||iTWIST'14, international - Traveling Workshop on Interactions between Sparse models and Technology|
|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||iTWIST'14 international Traveling Workshop on Interactions between Sparse models and Technology|
|Period||27/08/14 → 29/08/14|
Bibliographical noteLaurent Jacques
- Compressed sensing
- Total variation
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SRP11: Strategic Research Programme: Processing of large scale multi-dimensional, multi-spectral, multi-sensorial and distributed data (M³D²)
1/11/12 → 31/10/22
1/06/14 → 31/05/19