  We apply the OSCAR (octagonal selection and clustering algorithms for
regression) in recovering group-sparse matrices (two-dimensional---2D---arrays)
from compressive measurements. We propose a 2D version of OSCAR (2OSCAR)
consisting of the $\ell_1$ norm and the pair-wise $\ell_{\infty}$ norm, which
is convex but non-differentiable. We show that the proximity operator of 2OSCAR
can be computed based on that of OSCAR. The 2OSCAR problem can thus be
efficiently solved by state-of-the-art proximal splitting algorithms.
Experiments on group-sparse 2D array recovery show that 2OSCAR regularization
solved by the SpaRSA algorithm is the fastest choice, while the PADMM algorithm
(with debiasing) yields the most accurate results.
