基于 Res-SEUnet 的 ECT 图像重建算法

IF 2.3 3区工程技术 Q2 ENGINEERING, MECHANICAL Flow Measurement and Instrumentation Pub Date : 2024-09-18 DOI:10.1016/j.flowmeasinst.2024.102688

Xiaozhao Li , Jing Liu , Yuanyuan Li , Guoqiang Liu , Jiacheng Wei , Zhiguang Lyu

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引用次数: 0

摘要

本文提出了一种用于 ECT 图像重建的 Res-SEUnet 算法，以解决高密度和小尺寸介质的 ECT（电容断层扫描）重建图像中的伪影和介质边界模糊问题。该算法使用卷积神经网络提取 LBP 图像的细节特征，并恢复 ECT 反转图像的边缘细节。在 Comsol 和 Matlab 中构建的各种复杂多媒体数据集上进行了模拟实验。测试集的结果表明，基于 Res-SEUnet 的 ECT 重建算法能在各种复杂多媒体数据集上获得更好的重建效果：下载高清图片 (140KB)Download：下载全尺寸图像

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ECT image reconstruction algorithm based on Res-SEUnet

A Res-SEUnet algorithm for ECT image reconstruction is proposed to address the problem of artifacts and blurring of media boundaries in ECT (capacitance tomography) reconstructed images of high-density and small-size media. A convolutional neural network is used to extract the detail features of the LBP image and recover the edge details of the ECT inverted image. Simulation experiments are performed on various complex multi-media datasets built in Comsol and Matlab. The results of the test set show that the Res-SEUnet based ECT reconstruction algorithm can by better reconstruction results on various complex multimedia datasets.

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来源期刊

Flow Measurement and Instrumentation 工程技术-工程：机械

CiteScore

4.30

自引率

13.60%

发文量

123

审稿时长

6 months

期刊介绍： Flow Measurement and Instrumentation is dedicated to disseminating the latest research results on all aspects of flow measurement, in both closed conduits and open channels. The design of flow measurement systems involves a wide variety of multidisciplinary activities including modelling the flow sensor, the fluid flow and the sensor/fluid interactions through the use of computation techniques; the development of advanced transducer systems and their associated signal processing and the laboratory and field assessment of the overall system under ideal and disturbed conditions. FMI is the essential forum for critical information exchange, and contributions are particularly encouraged in the following areas of interest: Modelling: the application of mathematical and computational modelling to the interaction of fluid dynamics with flowmeters, including flowmeter behaviour, improved flowmeter design and installation problems. Application of CAD/CAE techniques to flowmeter modelling are eligible. Design and development: the detailed design of the flowmeter head and/or signal processing aspects of novel flowmeters. Emphasis is given to papers identifying new sensor configurations, multisensor flow measurement systems, non-intrusive flow metering techniques and the application of microelectronic techniques in smart or intelligent systems. Calibration techniques: including descriptions of new or existing calibration facilities and techniques, calibration data from different flowmeter types, and calibration intercomparison data from different laboratories. Installation effect data: dealing with the effects of non-ideal flow conditions on flowmeters. Papers combining a theoretical understanding of flowmeter behaviour with experimental work are particularly welcome.