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1.
Electrical resistance tomography (ERT) is a promising measurement technique in industrial process imaging. However, image reconstruction in ERT is an ill-posed inverse problem. Regularization methods have been developed to solve the ill-posed inverse problem. Since the penalty term is a form of L2-norm, Tikhonov regularization method guarantees the stability of the solution, but it always makes the image edge oversmoothed. Total variation (TV) regularization method has good ability of preserving image edges. A hybrid regularization method, which combines Tikhonov with TV regularization method, is proposed to get better reconstructed images. The choice of the adaptive weighted parameter between TV and Tikhonov penalty term has been discussed in detail. In the proposed hybrid regularization method, the function of conductivity gradients is used as the adaptive weighted parameter to control automatically the weighting between the penalty terms from TV and Tikhonov regularization. For the model with sharp edges, the proportion of the penalty term from TV regularization is increased to preserve the edges, while for the model with smooth edges, the proportion of penalty term from Tikhonov regularization is increased to make the solution stable and robust to noise. Both simulation and experimental results of Tikhonov, TV and hybrid regularization method are shown respectively, which indicates that the hybrid regularization method can improve the reconstruction quality with sharp edges and is more robust to noise, and it is applicable for models with different edge characteristic. 相似文献
2.
Electrical resistance tomography (ERT) reconstructs the conductivity distribution from the boundary changes of electrical measurements. The inverse problem of ERT is seriously ill-posed where regularization methods are needed to treat this ill-posedness. A proper choice of regularization parameter which controls the degree of smoothing is very important for these regularization methods. Although have been a variety of methods, such as L-curve method, to choose a reasonable parameter for the problem, these methods usually result in a scalar parameter which cannot distinctly express the spatial characteristic of the conductivity distribution. So a spatially adaptive regularization parameter choice method is proposed for regularizing the inverse problem of ERT based on Tikhonov regularization. Since large regularization parameters can stabilize and smoothen the solution, while small regularization parameters can approximate and sharpen the solution, the proposed method adaptively updates the regularization parameters during the iteration process and provides spatially varying parameter for each pixel of the reconstructed image. When the iteration is stopped, large regularization parameters for the smooth background region and small regularization parameters for the object region can be obtained. The method is discussed using simulated data for some typical conductivity distributions, and further applied to the analysis of real measurement data acquiring from the practical system. The results demonstrate that flexible regularization parameter vectors can be achieved for different distributions and the strength of regularization is adaptively provided for different regions in a specific distribution. The adaptive method achieves an efficient and reliable regularization solution and has outstanding performance in noise immunity especially in smooth background regions. 相似文献
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Image reconstruction algorithms play an important role in practical applications of electrical capacitance tomography. In the present paper, a combined image reconstruction method is proposed, which takes the results of Landweber algorithm as the constraint condition of Tikhonov algorithm's regularization parameter, calculates the regular parameter, inverts the inverse matrix of sensitivity matrix, and finally obtains the dielectric constant distribution; thus, reconstructed images with improved clarity were obtained. Simulation test are carried out to evaluate and analyze the proposed method from image error, correlation coefficient, image reconstruction time, and anti-noise ability. The results revealed that the Tikhonov regularization algorithm had excellent anti-noise ability; thus, it significantly improved the clarity of reconstructed images and clearly distinguished the multi-phase flow pattern and distribution. 相似文献
4.
两相流测量中电容层析成像图像重建的广义逆最小模解与线性反投影和迭代法的比较 总被引：10，自引：2，他引：10
本文根据电容层析成像ECT(ElectricalCapacitanceTomography)线性化了的电容与图像的关系,求出图像向量的广义逆最小模解。以试验数据对广义逆最小模解的图像重建能力进行研究。通过对广义逆最小模解、线性反投影法和迭代法三者的比较,了解了电容—图像线性方程的解不适宜电容层析图像重建的特点、线性反投影处理复杂物体分布图象的局限性,以及迭代法的优越性。同时发现了不加控制的迭代结果向广义逆最小模解结果趋进的现象。 相似文献
5.
Compared with electrical resistance tomography, capacitively coupled electrical resistance tomography (CCERT) is preferred since it avoids problems of electrode corrosion and electrode polarization. However, reconstruction of conductivity distribution is still a great challenge for CCERT. To improve reconstruction quality, this work proposes a novel image reconstruction method based on total fractional-order variation regularization. Simulation work is conducted and reconstruction of several typical models is studied. Robustness of the proposed method to noise is also conducted. Additionally, the performance of the proposed reconstruction method is quantitatively evaluated. We have also carried out phantom experiment to further verify the effectiveness of the proposed method. The results demonstrate that the quality of reconstruction has been largely improved when compared with the images reconstructed by Landweber, Newton-Raphson and Tikhonov methods. The inclusion is more accurately reconstructed and the background is much clearer even under the impact of noise. 相似文献
6.
Image reconstruction for electrical capacitance tomography (ECT) is to retrieve the permittivity distribution of materials inside the sensor from the capacitance measurements outside. It is a typical inverse problem and has long been a challenge for its nonlinearity and ill-posedness. This paper discusses the application of Tikhonov regularization, widely used for ill-posed problems, to the image reconstruction for electrical capacitance tomography. Two methods using different regularizations are investigated, which are the standard Tikhonov regularization and the Tikhonov regularization based on the second order derivative operator. Particularly, a combined method using the linear back projection (LBP) result as the prior constraint for the Tikhonov regularization with the second order derivative operator is suggested. Simulation and experiment results show that this combined method takes advantages from both the linear back projection and the Tikhonov regularization and provides reconstructions better than those from the LBP and the Tikhonov regularization. In addition, considering the essence that the Tikhonov regularization can be described as a spectral filter characterized by its corresponding window function, we propose the possibility of applying other window functions to the ECT image reconstruction, which include the Gauss window, the Hanning window, the Blackman window, and the cosine window. Results also show the feasibility of using window functions as regularization, which presents a new strategy for the regularization of ECT image reconstruction. 相似文献
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Imaging objects in electrical capacitance tomography (ECT) measurement are often in a dynamic evolution process, and exploiting the spatial–temporal properties of the dynamic reconstruction objects is crucial for the improvement of the reconstruction quality. Based on the multiple measurement vectors, in this paper a robust dynamic reconstruction model that incorporates the ECT measurement information and the dynamic evolution information of a dynamic object, in which a series of dynamic images is cast as a third-order tensor that the first two dimensions are space and the third is time, is proposed. Under the considerations of the two-dimensional spatial structure property of a difference image and the spatial–temporal property of a third-order image tensor, a new objective functional that fuses the ECT measurement information, the dynamic evolution information, the temporal constraint, the spatial constraint, the low rank constraint of a difference image and the low n-rank constraint of a third-order tensor is proposed, where the images are reconstructed by a batching pattern. The split Bregman iteration (SBI) algorithm is developed for solving the proposed objective functional. Numerical simulations are implemented to demonstrate the advantages of the proposed algorithm on improving the reconstruction quality and the robustness. 相似文献
9.
In this paper, a direct Landweber method is proposed to estimate on-line resistivity distribution of two-phase flows using electrical resistance tomography. The proposed method is formulated such that the iterative Landweber method is modified for on-line computation and the resistivity distribution is estimated directly by multiplying the measured data with a weighting matrix that is computed off-line. Moreover, to improve the reconstruction performance, adaptive step-lengths for the proposed method are computed. Numerical simulations and phantom experiments have been carried out to evaluate the performance of the proposed method. 相似文献
10.
Electrical resistance tomography (ERT) is an important branch of process tomography (PT), which has been developed for decades. Image reconstruction is a critical step in ERT, where the object of reconstruction is the conductivity distribution of the measured field. Traditional algorithms cannot accurately establish the mapping between the measured voltage and conductivity distribution. With the development of machine learning, the convolutional neural network (CNN) has become a new image reconstruction method. Specific results have been achieved in ERT image reconstruction using CNNs. This study proposes a one-dimensional multi-branch convolutional neural network (1D-MBCNN) for ERT image reconstruction, which could retain the 1D spatial structure of the measured voltage and adaptively and efficiently extract feature information. COMSOL software and the PyTorch framework are used to build the dataset and train the neural network model, respectively. The advantages of the multi-branch structure and the effectiveness of the attention mechanism in ERT image reconstruction are verified by RIE and CC. We also evaluated the practicality of this method in the ERT system. Based on the results of different experiments, the method proposed in this paper has good imaging accuracy, noise resistance, generalization ability, and robustness. 相似文献
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The image reconstruction of conductivity distribution in electrical impedance tomography (EIT) is a seriously ill-posed inverse problem. To cope with the problem, it is recognized that the regularization method is an effective approach. In this paper, an adaptive non-convex hybrid total variation (ANHTV) regularization method is proposed to reconstruct the conductivity distribution in EIT. The iterative reweighted least squares algorithm and the iterative alternating direction method of multipliers algorithm are developed to solve the ANHTV-based inverse model in the image reconstruction. Besides, all the parameters utilized in the inverse model are adaptively selected. To validate the advantage of the proposed method, extensive numerical simulation and experimental work have been carried out. Also, qualitative and quantitative comparisons with two convex TV-based regularization methods are conducted. The results show that the proposed method is more advantageous in terms of staircase effect suppression, edge information preservation and noise resisting in the image reconstruction. 相似文献
13.
Capacitively Coupled Electrical Resistance Tomography (CCERT), which is on the basis of Capacitively Coupled Contactless Conductivity Detection (C4D), is a novel electrical tomography technique. As a developing technique, more research work should be undertaken. This work focuses on the study of image reconstruction algorithm of CCERT. Combining Tikhonov regularization principle and Simultaneous Iterative Reconstruction Technique (SIRT), a new hybrid image reconstruction algorithm is proposed. Tikhonov regularization is introduced to obtain the initial reconstructed image. SIRT is used to obtain the final reconstructed image. With a 12-electrode CCERT prototype, image reconstruction experiments are carried out. Experimental results show that the images reconstructed by the proposed image reconstruction algorithm are satisfactory and are in accord with the actual distributions of two-phase flows. The research work also indicates that the proposed image reconstruction algorithm is more suitable for image reconstruction of CCERT, comparing with the conventional image reconstruction algorithms of Electrical Capacitance Tomography (ECT) and Electrical Resistance Tomography (ERT). 相似文献
14.
Compared to general room-temperature fluids, the characteristics of cryogenic fluids, as well as the complexity of the cryogenic environment, pose greater challenges for reconstruction algorithms for Electrical Capacitance Tomography (ECT). Based on deep learning, a hybrid model is proposed for cryogenic fluid ECT image reconstruction in this study. The multi-head self-attention mechanism is employed to initially establish the mapping of capacitance to the image, and then an improved U-net-like convolution neural network is presented to perform deep feature extraction and image reconstruction. The ConvNeXt block is adopted for multi-level feature extraction, and a separate downsampling layer is used to replace the pooling layer. A dataset covering a variety of two-phase typical flow patterns and irregular flow patterns is built for training. A capacitance vector and an image of phase distribution are included in each sample. Extensive numerical experiments are carried out on the trained model. The results show that the model can accurately predict phase distribution and produce a clear interface. Finally, the model was successfully applied in cryogenic experiment to obtain the phase distribution image of liquid nitrogen stratified flow. 相似文献
15.
The void fraction is one of the key parameters in the measurement of gas/liquid two-phase flow. It can be derived from the absolute conductivity distribution based on Maxwell׳s theory. With Electrical Resistance Tomography (ERT) technology, the absolute conductivity distribution is obtained by multiplying the relative conductivity image with the reference conductivity which is conventionally the liquid conductivity of a gas/liquid flow. Unfortunately the liquid conductivity is not always available. Therefore, a conductivity fitting method is proposed in this paper, to find an optimal reference conductivity, which will be used in substituting the liquid conductivity to reconstruct the quasi-absolute conductivity image. The optimal reference conductivity fitting method is proposed and validated by simulation and experiments under certain flow regimes, e.g. slug flow, annular flow and bubbly flow. The simulation and experimental results show that, independent from prior-knowledge, the fitted quasi-homogenous conductivity is close to the average conductivity of the sensing field. It also leads to a much more accurate estimation of void fraction than the conventional method using liquid conductivity as the reference. With the proposed method, the ERT technique can play a more significant role in the measurement of multiphase flow (MPF). 相似文献
16.
In order to solve the inverse solution for conductivity distribution in electrical impedance tomography, the one-step Gauss–Newton method is usually employed. Major computational time is involved in the calculation of inverse term of the Jacobian matrix and the complexity increases with the number of electrodes and finite elements. Therefore, to reduce the computational time, the inverse term is replaced with a summation term based on the eigenvalue and eigenvector in the inverse solver. In this paper, a fast inversion method using eigenvalue and eigenvector is developed to monitor the conductivity distribution. Therefore, using the proposed method the computation of inverse matrix is avoided resulting in decrease of the on-line computational time. Numerical simulations and experiments have been carried out to evaluate the performance of the proposed method. 相似文献
17.
Practical applications of the electrical capacitance tomography (ECT) rely mainly on the effectiveness of reconstruction algorithms. In this paper the solution of the inverse problem with the focus on the ECT imaging is reformulated to be an optimization problem by introducing a new loss function with regularizes encoding multiple features of solution. An iterative scheme that decomposes a complex optimization problem into several simpler sub-problems is developed to solve the proposed loss function, in which the linearization approximation and the acceleration strategy are introduced to improve numerical performances. Numerical experiments validate the effectiveness of the proposed imaging method in tackling the ECT inverse problem. 相似文献
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Process tomography (PT) techniques have been developed rapidly for visualizing the internal behavior of industrial processes, e.g. multi-phase flow measurement. Most of tomography systems employ a single measurement technique, such as computerized tomography (CT), optical tomography (OT), electrical resistance tomography (ERT) or electrical capacitance tomography (ECT). It is now possible to fit two or more tomographic systems to an industrial process. Detailed information from different modalities can be gained by inspection of separate tomographs, and the advantage of the strongest features provided by each unit can be taken. A combined tomogram can be produced of superior quality to any of the separate tomograms. To maximize the information available from the combined tomographic system, data fusion is the better option. In this paper, a dual-mode tomography system based on capacitance sensor and gamma sensor was developed to capture oil–gas two-phase flow. The two modalities can work at the same time. Two fusion methods, namely image fusion method and data fusion method, are proposed. Both simulation and static experiments for oil–gas two-phase flow were conducted. The reconstruction results of different fusion methods and modalities were compared and discussed. 相似文献
20.
Electrical capacitance tomography (ECT) is a visualization measurement method for two-phase flow. Imaging permittivity distributions using electrical capacitance tomography has always been one of the most significant issues studied by scholars, and the algorithm will have a great impact on the accuracy of image reconstruction result. This paper applies simulated annealing (SA) algorithm to image reconstruction in ECT. However, some parameters of SA algorithm need to be optimized in order to obtain better reconstructed images in ECT. The influence of different parameter values in SA algorithm for image reconstruction in ECT is studied, and a set of optimal parameters of the SA algorithm is obtained based on the orthogonal experimental design method in this paper. At the same time, simulation and static experiments are conducted. Reconstructed images by SA algorithm with optimized parameter are compared with the linear back projection (LBP) and Landweber iterative algorithms. The results show that better images can be obtained for typical oil-gas two-phase flow using SA algorithm. The quality and shape fidelity of reconstructed image for the central object are obviously improved. 相似文献