  A bilevel training scheme is used to introduce a novel class of regularizers,
providing a unified approach to standard regularizers $TV$, $TGV^2$ and
$NsTGV^2$. Optimal parameters and regularizers are identified, and the
existence of a solution for any given set of training imaging data is proved by
$\Gamma$-convergence. Explicit examples and numerical results are given.
