A signal processing interpretation of noise-reduction convolutional
  neural networks
Luis A. Zavala-Mondragón Peter H. N. de With Fons van der Sommen
Encoding-decoding CNNs play a central role in data-driven noise reduction and
can be found within numerous deep-learning algorithms. However, the development
of these CNN architectures is often done in ad-hoc fashion and theoretical
underpinnings for important design choices is generally lacking. Up to this
moment there are different existing relevant works that strive to explain the
internal operation of these CNNs. Still, these ideas are either scattered
and/or may require significant expertise to be accessible for a bigger
audience. In order to open up this exciting field, this article builds
intuition on the theory of deep convolutional framelets and explains diverse ED
CNN architectures in a unified theoretical framework. By connecting basic
principles from signal processing to the field of deep learning, this
self-contained material offers significant guidance for designing robust and
efficient novel CNN architectures.