  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.
