Electromagnetic Tomography Image Reconstruction Research Articles

Influence on Sample Determination for Deep Learning Electromagnetic Tomography.

Deep learning (DL) has been frequently applied in the image reconstruction of electromagnetic tomography (EMT) in recent years. It offers the potential to achieve higher-quality image reconstruction. Among these, research on samples is relatively scarce. Samples are the cornerstone for both large and small models, which is easy to ignore. In this paper, a deep learning electromagnetic tomography (DL-EMT) model with nine elements is established. Complete simulation and experimental samples are obtained based on this model. On the sample sets, the reconstruction quality is observed by adjusting the size and configuration of the training set. The Mann-Whitney U test shows that beyond a certain point, the addition of more samples to the training data fed into the deep learning network does not result in an obvious improvement statistically in the quality of the reconstructed images. This paper proposes a CC-building method for optimizing a sample set. This method is based on the Pearson correlation coefficient calculation, aiming to establish a more effective sample base for DL-EMT image reconstruction. The statistical analysis shows that the CC-building method can significantly improve the image reconstruction effect in a small and moderate sample size. This method is also validated by experiments.

In Situ Damage Monitoring of CFRPs by Electromagnetic Tomography With the Compatible Multitemplate Supervised Descent Method

In this paper, an in-situ damage detection framework for carbon fiber-reinforced plastics (CFRPs) is proposed. The methodology involves the design of an electromagnetic tomography (EMT) sensor and developing an absolute image reconstruction method for electromagnetic tomography. A linear EMT sensor is designed for in-situ structural health monitoring of CFRPs tensile load test specimens. A compatible multi-template supervised descent method (cmt-SDM) based on a normal mixture distribution model of pixels is proposed for EMT image reconstruction. The cmt-SDM was developed to overcome the ill-posed problem in absolute image reconstruction. Simulation and experimental results show that the method proposed in this paper can be employed to monitor and evaluate the local damage degree of CFRPs specimens under quasi-static tensile loading. In addition, when the local fiber-breaking ratio reaches 40%, the damage location can be accurately reflected by the imaging results. Compared with the traditional SDM method, the method proposed in this paper shows smaller estimation error and delivers more accurate reconstructed images.

Image reconstruction algorithm of electromagnetic tomography based on fractional Kalman filter

Aiming at the problem of low quality of image reconstruction of electromagnetic tomography (EMT), in this paper, an image reconstruction algorithm of EMT based on fractional Kalman filter (FKF) is proposed. Firstly, the principle of EMT and the principle of state equation of FKF are expound respectively. FKF is often used in the state estimation of nonlinear systems. There is a nonlinear relationship between the object field distribution and the sensor signal in the EMT. Therefore, according to this feature, FKF is applied to the image reconstruction algorithm of EMT. The image reconstruction process of EMT is regarded as the state estimation process of FKF, the normalized measurement voltage is taken as the observation value, and the sensitivity matrix is taken as the measurement matrix. To establish the nonlinear state estimation equation of the FKF and a priori estimation error covariance equation in the EMT, the gray value of image obtained by LBP is used as the initial value of the state estimation, a prior estimation state vector and a priori estimation error covariance matrix are obtained by prediction update, the Kalman filter gain and the posterior estimation error covariance matrix are obtained by the correction feedback process. After repeated iterations, the final state vector, i.e. reconstructed image of EMT is obtained. Finally, simulation experiments are carried out for seven different flow patterns. The results show that the image error and correlation coefficients of the reconstructed image of this algorithm are better than traditional algorithms such as LBP, Landweber, Kalman filter, and have better anti-noise effect than Kalman filter. Therefore, the image reconstruction algorithm of FKF is a new method and means to study the image reconstruction of EMT.

Image Reconstruction Based on Sequential Monte Carlo Principle for EMT

Due to the single and limited prior information in electromagnetic tomography (EMT), the existing reconstruction methods are often unable to reconstruct the image with high accuracy for different kinds of flow patterns. In this article, an image reconstruction algorithm based on the sequential Monte Carlo principle for EMT is proposed. It can increase the diversity of samples to extract more image features for EMT images reconstruction and maintain a high image reconstruction effect for different flow patterns. First, the EMT image reconstruction process is modeled as a dynamic time-varying system to search for the optimal estimation in state space. At the same time, controllable adjustment of probability density distribution can improve the reconstruction effect under different flow patterns. Second, samples are obtained from the initial sample space, and their weights are updated according to the samples’ information. The numbers of effective samples and the diversity of samples are increased by resampling. Finally, the system state estimation is calculated according to the samples and their updated weights, and the system state is updated iteratively until the optimal solution is obtained. Simulation results have shown that the proposed method is superior to other existing methods in image error and correlation coefficient, and has better imaging quality.

Research on image reconstruction algorithms based on autoencoder neural network of Restricted Boltzmann Machine (RBM)

Aiming at the problem of low quality in image reconstruction of traditional image reconstruction algorithm of electromagnetic tomography(EMT), an EMT image reconstruction algorithm based on autoencoder neural network of Restricted Boltzmann Machine (RBM) is proposed. Firstly, the basic principles of EMT system and autoencoder neural network are analyzed. Autoencoder neural network is a deep learning model, which contains two parts: encoder and decoder. The encoding process of the encoder is equivalent to the object field detection process in the EMT system; the decoding process of the decoder is equivalent to the image reconstruction process. On this basis, an autoencoder neural network model is built. In this model, the RBM is used for layer by layer pre-training to obtain the initial weight and offset, and the global weight and offset are adjusted by BP algorithm. The parameter file generated in the trained autoencoder neural network is used to construct a decoder. Finally, the detected voltage value output by the EMT system is input into the decoder network to obtain the reconstructed image of the EMT. Furthermore, data with Gaussian noise and data regarding flow pattern not in training dataset are used to test the generalization ability and practicability of the network, respectively. The experimental results show that the method in this paper is a kind of EMT image reconstruction method with higher accuracy, which also provides a new means for EMT image reconstruction.

Image reconstruction based on frequency domain feature extraction for EMT

Image reconstruction of electromagnetic tomography (EMT) is often ill-posed due to the limited prior information about imaging features, leading to low reconstruction accuracy. In this paper, a novel image reconstruction method based on frequency domain feature extraction for EMT is proposed to improve the reconstruction accuracy. The two-dimensional EMT image is first modeled as a one-dimensional finite-length streams of Dirac signal, which is a typical signal with finite rate of innovation (FRI). Then the reconstruction results of the recent algorithms, such as the LBP, Landweber and TV algorithms, can be also modeled as FRI signals. The frequency domain feature of such signals can be extracted with a distributed FRI sampling system. Based on the obtained FRI samples, a new EMT measurement equation is established, and a joint image reconstruction algorithm is proposed, to improve the quality of the reconstructed image. Finally, simulation results have shown that the proposed method outperforms the related methods with better indicators such as image error and correlation coefficient.

Application of particle filtering algorithm in image reconstruction of EMT

To improve the image quality of electromagnetic tomography (EMT), a new image reconstruction method of EMT based on a particle filtering algorithm is presented. Firstly, the principle of image reconstruction of EMT is analyzed. Then the search process for the optimal solution for image reconstruction of EMT is described as a system state estimation process, and the state space model is established. Secondly, to obtain the minimum variance estimation of image reconstruction, the optimal weights of random samples obtained from the state space are calculated from the measured information. Finally, simulation experiments with five different flow regimes are performed. The experimental results have shown that the average image error of reconstruction results obtained by the method mentioned in this paper is 42.61%, and the average correlation coefficient with the original image is 0.8706, which are much better than corresponding indicators obtained by LBP, Landweber and Kalman Filter algorithms. So, this EMT image reconstruction method has high efficiency and accuracy, and provides a new method and means for EMT research.