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.

这篇文章通过将信号处理的基本原理与深度学习领域相连接，在统一的理论框架下，建立了对深度卷积框图的直观理解，解释了多样的编码 - 解码卷积神经网络架构，并提供了设计稳健高效的新颖卷积神经网络架构的重要指导。