  Computer vision and image processing address many challenging applications.
While the last decade has seen deep neural network architectures
revolutionizing those fields, early methods relied on 'classic', i.e.,
non-learned approaches. In this study, we explore the differences between
classic and deep learning (DL) algorithms to gain new insight regarding which
is more suitable for a given application. The focus is on two challenging
ill-posed problems, namely faint edge detection and multispectral image
registration, studying recent state-of-the-art DL and classic solutions. While
those DL algorithms outperform classic methods in terms of accuracy and
development time, they tend to have higher resource requirements and are unable
to perform outside their training space. Moreover, classic algorithms are more
transparent, which facilitates their adoption for real-life applications. As
both classes of approaches have unique strengths and limitations, the choice of
a solution is clearly application dependent.
