辽宁大学学报（自然科学版）2025，Vol.52Issue(2)：97-110,14.
基于深度学习的电容层析成像图像重建算法综述
Overview of Deep Learning Based Image Reconstruction Algorithms for Electrical Capacitance Tomography
摘要
Abstract
Firstly,the research significance,development history and characteristics of electrical capacity tomography(ECT)were introduced.Then,the advantages and disadvantages of traditional ECT image reconstruction algorithms were analyzed,and the research characteristics of ECT image reconstruction algorithms based on convolutional neural networks,autoencoder neural networks,long short-term memory neural networks,U-Net neural networks,Transformer neural networks,and other neural networks were summarized.The shortcomings of current deep learning based ECT image reconstruction algorithms were analyzed,and based on this analysis,the future research directions and development trends of deep learning based ECT image reconstruction algorithms were proposed.关键词
深度学习/电容层析成像/卷积神经网络/图像重建算法Key words
deep learning/electrical capacitance tomography(ECT)/convolutional neural network/image reconstruction algorithm分类
信息技术与安全科学引用本文复制引用
吴新杰,刘延东,刘世兴..基于深度学习的电容层析成像图像重建算法综述[J].辽宁大学学报（自然科学版）,2025,52(2):97-110,14.基金项目
国家自然科学基金项目(12372002,12232009) （12372002,12232009）
