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。

噪声抑制卷积神经网络的信号处理解释

A signal processing interpretation of noise-reduction convolutional  neural networks

X-ray computed tomography (CT) using sparse projection views is a recent
approach to reduce the radiation dose. However, due to the insufficient
projection views, an analytic reconstruction approach using the filtered back
projection (FBP) produces severe streaking artifacts. Recently, deep learning
approaches using large receptive field neural networks such as U-Net have
demonstrated impressive performance for sparse- view CT reconstruction.
However, theoretical justification is still lacking. Inspired by the recent
theory of deep convolutional framelets, the main goal of this paper is,
therefore, to reveal the limitation of U-Net and propose new multi-resolution
deep learning schemes. In particular, we show that the alternative U- Net
variants such as dual frame and the tight frame U-Nets satisfy the so-called
frame condition which make them better for effective recovery of high frequency
edges in sparse view- CT. Using extensive experiments with real patient data
set, we demonstrate that the new network architectures provide better
reconstruction performance.

本文旨在揭示 U-Net 的局限性并提出新的多分辨率深度学习方案，具体而言，我们通过实验表明，新的网络架构可以提供更好的重建性能，并且我们探讨了 U-Net 替代方案，如双框架和紧框架 U-Net 满足所谓的框条件，使其更好地实现高频边缘的有效恢复。