Total Variation Minimization in Compressed Sensing
Information Theory
2017-11-06 v2 math.IT
Abstract
This chapter gives an overview over recovery guarantees for total variation minimization in compressed sensing for different measurement scenarios. In addition to summarizing the results in the area, we illustrate why an approach that is common for synthesis sparse signals fails and different techniques are necessary. Lastly, we discuss a generalizations of recent results for Gaussian measurements to the subgaussian case.
Keywords
Cite
@article{arxiv.1704.02105,
title = {Total Variation Minimization in Compressed Sensing},
author = {Felix Krahmer and Christian Kruschel and Michael Sandbichler},
journal= {arXiv preprint arXiv:1704.02105},
year = {2017}
}
Comments
23 pages, 2 figures