  Deep learning-based image processing is capable of creating highly appealing
results. However, it is still widely considered as a "blackbox" transformation.
In medical imaging, this lack of comprehensibility of the results is a
sensitive issue. The integration of known operators into the deep learning
environment has proven to be advantageous for the comprehensibility and
reliability of the computations. Consequently, we propose the use of the
locally linear guided filter in combination with a learned guidance map for
general purpose medical image processing. The output images are only processed
by the guided filter while the guidance map can be trained to be task-optimal
in an end-to-end fashion. We investigate the performance based on two popular
tasks: image super resolution and denoising. The evaluation is conducted based
on pairs of multi-modal magnetic resonance imaging and cross-modal computed
tomography and magnetic resonance imaging datasets. For both tasks, the
proposed approach is on par with state-of-the-art approaches. Additionally, we
can show that the input image's content is almost unchanged after the
processing which is not the case for conventional deep learning approaches. On
top, the proposed pipeline offers increased robustness against degraded input
as well as adversarial attacks.
