Image Reconstruction in Electrical Capacitance Tomography Based on Deep Neural Networks

Deabes, Wael; Khayyat, Khalid M. Jamil

doi:10.1109/jsen.2021.3116164

Cited by 23 publications

(10 citation statements)

References 45 publications

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“…An assortment of flow patterns have been set up to test the generalization ability of the proposed model. Figure 8 shows the compassion results, where the real phantoms are shown in the first column, and the reconstructed images from the LBP, iterative Tikhonov, ILM, CNN [22,23], LSTM-IR [24], and ARE-ECT algorithms are contained in the other columns, respectively. The hyperparameters of the Tikhonov and ILM algorithms were selected empirically.…”

Section: Qualitative Results On Simulation Test Datasetmentioning

confidence: 99%

“…DNN methods have been utilized in many fields due to their ability to map complex nonlinear functions [20,21]. DNN algorithms have been transferred and adapted such as in image reconstruction methods based on the convolutional neural network (CNN) [22], multi-scale CNNs [23], long short-term memory (LSTM) [24], and autoencoder [25]. To solve the forward problem and to estimate the capacity measures, Deabes et al used a capacitance artificial neural network (CANN) system [26,27].…”

Section: Introductionmentioning

confidence: 99%

“…Thanks to its ability to effectively use specific geometric relationships hidden in commonly used unstructured grid models, the authors in [28] proposed to use the graph convolutional network(s) (GCN), to increase the quality of the ECT image. Moreover, a long short-term memory image reconstruction (LSTM-IR) algorithm was implemented to map the capacitance measurements to accurate material distribution images [24].…”

Section: Introductionmentioning

confidence: 99%

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Adversarial Resolution Enhancement for Electrical Capacitance Tomography Image Reconstruction

Deabes

Abdel-Hakim

Bouazza

et al. 2022

Sensors

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High-quality image reconstruction is essential for many electrical capacitance tomography (CT) applications. Raw capacitance measurements are used in the literature to generate low-resolution images. However, such low-resolution images are not sufficient for proper functionality of most systems. In this paper, we propose a novel adversarial resolution enhancement (ARE-ECT) model to reconstruct high-resolution images of inner distributions based on low-quality initial images, which are generated from the capacitance measurements. The proposed model uses a UNet as the generator of a conditional generative adversarial network (CGAN). The generator’s input is set to the low-resolution image rather than the typical random input signal. Additionally, the CGAN is conditioned by the input low-resolution image itself. For evaluation purposes, a massive ECT dataset of 320 K synthetic image–measurement pairs was created. This dataset is used for training, validating, and testing the proposed model. New flow patterns, which are not exposed to the model during the training phase, are used to evaluate the feasibility and generalization ability of the ARE-ECT model. The superiority of ARE-ECT, in the efficient generation of more accurate ECT images than traditional and other deep learning-based image reconstruction algorithms, is proved by the evaluation results. The ARE-ECT model achieved an average image correlation coefficient of more than 98.8% and an average relative image error about 0.1%.

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Section: Qualitative Results On Simulation Test Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adversarial Resolution Enhancement for Electrical Capacitance Tomography Image Reconstruction

Deabes

Abdel-Hakim

Bouazza

et al. 2022

Sensors

Self Cite

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“…6 and 7, are in reasonable intervals, which confirms the high performance of the ECT_ResAE model. 9, where the real phantom is in the first column, the second column contains the modulated capacitance matrix of each phantom, and the others show the results from the LBP, iterative Tikhonov, ILM, CNN [30], LSTM-IR [35], and ECT_ResAE algorithms, respectively. The hyperparameters of the Tikhonov regularity algorithm are selected by try and error procedure.…”

Section: Qualitative Analysis On Testing Patternsmentioning

confidence: 99%

“…Deabes et al developed a highly reliable Long-Short Term Memory (ECT-LSTM-RNN) model to image the metal during the LFC industrial process [34]. Also, an LSTM-IR algorithm is implemented to map the capacitance measurements to accurate material distribution images [35].…”

Section: Introductionmentioning

confidence: 99%

Residual Autoencoder Deep Neural Network for Electrical Capacitance燭omography

Deabes¹,

Bouazza²

2022

Computers, Materials &Amp; Continua

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Great achievements have been made during the last decades in the field of Electrical Capacitance Tomography (ECT) image reconstruction. However, there is still a need to make these image reconstruction results faster and of better quality. Recently, Deep Learning (DL) is flourishing and is adopted in many fields. The DL is very good at dealing with complex nonlinear functions and it is built using several series of Artificial Neural Networks (ANNs). An ECT image reconstruction model using DNN is proposed in this paper. The proposed model mainly uses Residual Autoencoder called (ECT_ResAE). A large-scale dataset of 320 k instances have been generated to train and test the proposed ECT_ResAE model. Each instance contains two vectors; a distinct permittivity distribution and its corresponding capacitance measurements. The capacitance vector has been modulated to generate a 66×66 image, and represented to the ECT_ResAE as an input. The scalability and practicability of the ECT_ResAE network are tested using noisy data, new samples, and experimental data. The experimental results show that the proposed ECT_ResAE image reconstruction model provides accurate reconstructed images. It achieved an average image Correlation Coefficient (CC) of more than 99% and an average Relative Image Error (IE) around 8.5%.

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Neural-network-based reconstruction of spent-fuel radioactive distribution in nuclear waste cask

Liu,

Wu,

Chen

et al. 2024

Annals of Nuclear Energy

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Image Reconstruction in Electrical Capacitance Tomography Based on Deep Neural Networks

Cited by 23 publications

References 45 publications

Adversarial Resolution Enhancement for Electrical Capacitance Tomography Image Reconstruction

Adversarial Resolution Enhancement for Electrical Capacitance Tomography Image Reconstruction

Residual Autoencoder Deep Neural Network for Electrical Capacitance燭omography

Neural-network-based reconstruction of spent-fuel radioactive distribution in nuclear waste cask

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