  Semantic edge detection has recently gained a lot of attention as an image
processing task, mainly due to its wide range of real-world applications. This
is based on the fact that edges in images contain most of the semantic
information. Semantic edge detection involves two tasks, namely pure edge
detecion and edge classification. Those are in fact fundamentally distinct in
terms of the level of abstraction that each task requires, which is known as
the distracted supervision paradox that limits the possible performance of a
supervised model in semantic edge detection. In this work, we will present a
novel hybrid method to avoid the distracted supervision paradox and achieve
high-performance in semantic edge detection. Our approach is based on a
combination of the model-based concept of shearlets, which provides probably
optimally sparse approximations of a model-class of images, and the data-driven
method of a suitably designed convolutional neural netwok. Finally, we present
several applications such as tomographic reconstruction and show that our
approach signifiantly outperforms former methods, thereby indicating the value
of such hybrid methods for the area in biomedical imaging.
