{"title":"Wavelet-integrated deep neural networks: A systematic review of applications and synergistic architectures","authors":"Jiangtao Wu, Jiaqi Li, Jie Yang, Shuli Mei","doi":"10.1016/j.neucom.2025.131648","DOIUrl":null,"url":null,"abstract":"<div><div>Wavelet transforms, known for their exceptional capabilities in multi-resolution analysis, have garnered significant attention in the integration with deep neural networks to address key challenges in complex pattern analysis and recognition tasks. This review examines how the integration of wavelet transforms with emerging deep learning techniques has accelerated progress across various domains, such as image and video processing, graph and spatial-temporal data analysis. By integrating wavelets into traditional deep learning models, such as convolutional neural networks (CNNs), and emerging architectures like transformers and diffusion models, we show how these hybrid methods improve multi-scale feature representation, efficiency, and interpretability, while mitigating common deep learning limitations such as high computational costs and reduced robustness in multi-resolution analysis. We systematically address the synergy between wavelet transforms and deep learning, a topic underexplored in previous literature, and highlight the diverse strategies of wavelet integration—ranging from foundational methods to advanced neural network architectures—and conduct a comparative analysis of their performance in real-world applications. We also identify critical gaps and present directions for future research, particularly in the areas of adaptive, data-driven wavelet frameworks and their potential in generative modeling and domain adaptation.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"657 ","pages":"Article 131648"},"PeriodicalIF":6.5000,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neurocomputing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0925231225023203","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Wavelet transforms, known for their exceptional capabilities in multi-resolution analysis, have garnered significant attention in the integration with deep neural networks to address key challenges in complex pattern analysis and recognition tasks. This review examines how the integration of wavelet transforms with emerging deep learning techniques has accelerated progress across various domains, such as image and video processing, graph and spatial-temporal data analysis. By integrating wavelets into traditional deep learning models, such as convolutional neural networks (CNNs), and emerging architectures like transformers and diffusion models, we show how these hybrid methods improve multi-scale feature representation, efficiency, and interpretability, while mitigating common deep learning limitations such as high computational costs and reduced robustness in multi-resolution analysis. We systematically address the synergy between wavelet transforms and deep learning, a topic underexplored in previous literature, and highlight the diverse strategies of wavelet integration—ranging from foundational methods to advanced neural network architectures—and conduct a comparative analysis of their performance in real-world applications. We also identify critical gaps and present directions for future research, particularly in the areas of adaptive, data-driven wavelet frameworks and their potential in generative modeling and domain adaptation.
期刊介绍:
Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.