  Computed tomography is widely used to examine internal structures in a
non-destructive manner. To obtain high-quality reconstructions, one typically
has to acquire a densely sampled trajectory to avoid angular undersampling.
However, many scenarios require a sparse-view measurement leading to
streak-artifacts if unaccounted for. Current methods do not make full use of
the domain-specific information, and hence fail to provide reliable
reconstructions for highly undersampled data. We present a novel framework for
sparse-view tomography by decoupling the reconstruction into two steps: First,
we overcome its ill-posedness using a super-resolution network, SIN, trained on
the sparse projections. The intermediate result allows for a closed-form
tomographic reconstruction with preserved details and highly reduced
streak-artifacts. Second, a refinement network, PRN, trained on the
reconstructions reduces any remaining artifacts. We further propose a
light-weight variant of the perceptual-loss that enhances domain-specific
information, boosting restoration accuracy. Our experiments demonstrate an
improvement over current solutions by 4 dB.
