降噪卷积神经网络的信号处理解释:探索编解码cnn的数学公式

IF 9.4 1区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Signal Processing Magazine Pub Date : 2023-11-08 DOI:10.1109/MSP.2023.3300100
Luis Albert Zavala-Mondragón;Peter H.N. de With;Fons van der Sommen
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引用次数: 0

摘要

编码解码卷积神经网络(CNNs)在数据驱动的降噪中发挥着核心作用,可以在许多深度学习算法中找到。然而,这些CNN架构的开发通常是以临时的方式进行的,并且通常缺乏重要设计选择的理论基础。到目前为止,已有不同的相关著作致力于解释这些细胞神经网络的内部运作。尽管如此,这些想法要么分散,要么可能需要大量的专业知识才能吸引更多的受众。为了打开这个令人兴奋的领域,本文在深度卷积小帧(TDCF)理论的基础上建立了直觉,并在统一的理论框架中解释了不同的编码-解码(ED)CNN架构。通过将信号处理的基本原理与深度学习领域联系起来,这种独立的材料为设计稳健高效的新型CNN架构提供了重要指导。
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A Signal Processing Interpretation of Noise-Reduction Convolutional Neural Networks: Exploring the mathematical formulation of encoding-decoding CNNs
Encoding-decoding convolutional neural networks (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 an ad hoc fashion and theoretical underpinnings for important design choices are generally lacking. Up to now, there have been different existing relevant works that have striven 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. To open up this exciting field, this article builds intuition on the theory of deep convolutional framelets (TDCFs) and explains diverse encoding-decoding (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.
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来源期刊
IEEE Signal Processing Magazine
IEEE Signal Processing Magazine 工程技术-工程:电子与电气
CiteScore
27.20
自引率
0.70%
发文量
123
审稿时长
6-12 weeks
期刊介绍: EEE Signal Processing Magazine is a publication that focuses on signal processing research and applications. It publishes tutorial-style articles, columns, and forums that cover a wide range of topics related to signal processing. The magazine aims to provide the research, educational, and professional communities with the latest technical developments, issues, and events in the field. It serves as the main communication platform for the society, addressing important matters that concern all members.
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