Total Variation Reconstruction From Quasi-Random Samples Colas Schretter1,2 , Ignace Loris3 , Ann Dooms1,2 and Peter Schelkens1,2 1 Vrije Universiteit Brussel, Dept. of Electronics and Informatics, Pleinlaan 2, B-1050 Br - Low-discrepancy sequence - Document - PDFSEARCH.IO

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	Total Variation Reconstruction From Quasi-Random Samples Colas Schretter1,2 , Ignace Loris3 , Ann Dooms1,2 and Peter Schelkens1,2 1 Vrije Universiteit Brussel, Dept. of Electronics and Informatics, Pleinlaan 2, B-1050 Br Add to Reading List Document Date: 2014-02-26 11:10:13 Open Document File Size: 543,76 KB Share Result on Facebook City Philadelphia / Bruxelles / Brussels / Ghent / / Company International Journal / Sun / / Country Belgium / / IndustryTerm noise removal algorithms / proposed quasi-random algorithm / computing / cal medical imaging setup / imaging / primal-dual algorithm / / NaturalFeature Lena / / Organization National Science Foundation / / Person I. Loris / C. Verhoeven / Gaston Crommenlaan / / Position research associate / / PublishedMedium Physica D / / Technology proposed quasi-random algorithm / The algorithm / noise removal algorithms / primal-dual algorithm / tomography / medical imaging / / SocialTag Randomness Non-uniform random numbers Monte Carlo methods Numerical analysis Sampling Low-discrepancy sequence Peak signal-to-noise ratio Pseudo-random number sampling Inverse transform sampling Mathematics

Total Variation Reconstruction From Quasi-Random Samples Colas Schretter1,2 , Ignace Loris3 , Ann Dooms1,2 and Peter Schelkens1,2 1 Vrije Universiteit Brussel, Dept. of Electronics and Informatics, Pleinlaan 2, B-1050 BrAdd to Reading List