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.

翻译：编码-解码卷积神经网络在数据驱动的降噪中扮演核心角色，并广泛存在于众多深度学习算法中。然而，这类CNN架构的开发常采用特定方式，且关键设计选择的理论基础普遍缺失。目前存在多种相关重要研究试图解释这些CNN的内部运作机制，但这些观点要么分散零碎，要么需要相当的专业知识才能被更广泛的受众所理解。为开拓这一令人兴奋的研究领域，本文构建了深度卷积框架理论的直观理解，并在统一的理论框架下阐释了多种ED CNN架构。通过将信号处理的基本原理与深度学习领域相联系，这份自包含的材料为设计鲁棒且高效的新型CNN架构提供了重要指导。