  Depth maps captured with commodity sensors often require super-resolution to
be used in applications. In this work we study a super-resolution approach
based on a variational problem statement with Tikhonov regularization where the
regularizer is parametrized with a deep neural network. This approach was
previously applied successfully in photoacoustic tomography. We experimentally
show that its application to depth map super-resolution is difficult, and
provide suggestions about the reasons for that.
