Total Variation Reconstruction From Quasi-Random Samples

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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 languageEnglish
Title of host publicationiTWIST'14, international - Traveling Workshop on Interactions between Sparse models and Technology
EditorsLaurent Jacques
Pages57-58
Publication statusPublished - 2014
EventiTWIST'14 international Traveling Workshop on Interactions between Sparse models and Technology - Namur, Belgium
Duration: 27 Aug 201429 Aug 2014

Conference

ConferenceiTWIST'14 international Traveling Workshop on Interactions between Sparse models and Technology
CountryBelgium
CityNamur
Period27/08/1429/08/14

Bibliographical note

Laurent Jacques

Keywords

  • Compressed sensing
  • Total variation

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