{"title":"Advanced image reconstruction for electrostatic tomography in gas-solid two-phase flow based on convolutional autoencoder neural network","authors":"Jiahe Lyu, Xuezhen Cheng, Zhen Song, Jiming Li","doi":"10.1016/j.elstat.2024.103979","DOIUrl":null,"url":null,"abstract":"<div><div>The image reconstruction of flowing charged particles in gas-solid two-phase (GSTP) flow can be achieved through electrostatic tomography (EST). Accurate image reconstruction is crucial for detecting the movement patterns of the particles. In order to improve the quality of reconstructed images, a unique convolutional autoencoder neural network (CANN) is proposed. This study uses an image set generated by the linear backprojection (LBP) algorithm to train the CANN, which consists of an encoder and a decoder. The encoder utilizes convolutional and max-pooling layers to reduce the dimensionality of the images and extract key features, while the decoder restores and reconstructs the images through up-sampling and convolutional operations to closely approximate the reference image. To prevent overfitting, dropout layers are introduced after each max-pooling layer in the encoder. To verify the anti-noise capability of the network, Gaussian white noise ranging from 10 dB to 20 dB is added to the test set. The proposed CANN has been validated through simulations and experiments, demonstrating its effectiveness in overcoming noticeable artifacts and noise in reconstructed images when identifying GSTP flow patterns. Furthermore, it shows significant enhancements in imaging outcomes compared to conventional image reconstruction techniques and some current deep learning algorithms.</div></div>","PeriodicalId":54842,"journal":{"name":"Journal of Electrostatics","volume":"132 ","pages":"Article 103979"},"PeriodicalIF":1.9000,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Electrostatics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S030438862400086X","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The image reconstruction of flowing charged particles in gas-solid two-phase (GSTP) flow can be achieved through electrostatic tomography (EST). Accurate image reconstruction is crucial for detecting the movement patterns of the particles. In order to improve the quality of reconstructed images, a unique convolutional autoencoder neural network (CANN) is proposed. This study uses an image set generated by the linear backprojection (LBP) algorithm to train the CANN, which consists of an encoder and a decoder. The encoder utilizes convolutional and max-pooling layers to reduce the dimensionality of the images and extract key features, while the decoder restores and reconstructs the images through up-sampling and convolutional operations to closely approximate the reference image. To prevent overfitting, dropout layers are introduced after each max-pooling layer in the encoder. To verify the anti-noise capability of the network, Gaussian white noise ranging from 10 dB to 20 dB is added to the test set. The proposed CANN has been validated through simulations and experiments, demonstrating its effectiveness in overcoming noticeable artifacts and noise in reconstructed images when identifying GSTP flow patterns. Furthermore, it shows significant enhancements in imaging outcomes compared to conventional image reconstruction techniques and some current deep learning algorithms.
期刊介绍:
The Journal of Electrostatics is the leading forum for publishing research findings that advance knowledge in the field of electrostatics. We invite submissions in the following areas:
Electrostatic charge separation processes.
Electrostatic manipulation of particles, droplets, and biological cells.
Electrostatically driven or controlled fluid flow.
Electrostatics in the gas phase.