A hybrid regularization method combining Tikhonov with total variation for electrical resistance tomography

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.     

An adaptive Tikhonov regularization parameter choice method for electrical resistance tomography

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.     

An adaptive non-convex hybrid total variation regularization method for image reconstruction in electrical impedance tomography

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.     

Total fractional-order variation regularization based image reconstruction method for capacitively coupled electrical resistance tomography

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.     

Window function-based regularization for electrical capacitance tomography image reconstruction

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.     

Regularization parameter optimization based on the constraint of Landweber algorithm for electrical capacitance tomography

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.     

Image reconstruction algorithm for capacitively coupled electrical resistance tomography

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).     

Electrical resistance tomography image reconstruction based on one-dimensional multi-branch convolutional neural network combined with attention mechanism

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.     

Split Bregman iteration based image reconstruction algorithm for electrical capacitance tomography

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.     

基于两步迭代TV正则化的电阻抗图像重建算法

针对电阻抗层析成像(electrical impedance tomography,EIT)逆问题求解的欠定性和病态性,克服传统基于L2范数的Tikhonov正则化对介质边界的模糊效应,提出一种基于两步迭代的正则化图像重建算法.该算法采用具有良好保边性的总变差(total variation,TV)正则化函数,利用两步迭代法引入TV去噪算子,达到解的双重正则化效果.与传统最小二乘迭代算法、TV相关迭代算法相比,不仅保证了逆问题求解的稳定性,而且进一步提高了非连续分布介质区域成像的分辨能力,具有较好的成像精度.     

Sparsity-inspired image reconstruction for electrical capacitance tomography

We present a new image reconstruction method for Electrical Capacitance Tomography (ECT). ECT image reconstruction is generally ill-posed because the number of measurements is small whereas the image dimensions are large. Here, we present a sparsity-inspired approach to achieve better ECT image reconstruction from the small number of measurements. Our approach for ECT image reconstruction is based on Total Variation (TV) regularization. We apply an efficient Split-Bregman Iteration (SBI) approach to solve the problem. We also propose three metrics to evaluate image reconstruction performance, i.e., a joint metric of positive reconstruction rate (PRR) and false reconstruction rate (FRR), correlation coefficient, and a shape and location metric. The results on both synthetic and real data show that the proposed TV-SBI method can better preserve the edges of images and better resolve different objects within reconstructed images, as compared to a representative state-of-the-art ECT image reconstruction algorithm, Projected Landweber Iteration with Linear Back Projection initialization (LBP-PLI).     

Electrical resistance imaging of two-phase flow using direct Landweber method

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.     

电阻层析成像技术图像重建算法概述

电阻层析成像技术是近年来发展起来的一种截面分布式的检测技术．具有非侵入的，无辐射的，可视化的特点。在电阻层析成像技术中．图像重建是由测量到的边界电压重建出对象内部电阻率分布的过程．是最终实现ERT技术可视化测量的过程。图像重建算法．直接影响成像的效果。本文介绍了目前比较常用的图像重建算法的原理以及应用过程中的优缺点。     

Dimensionality reduced simultaneous iterative reconstruction technique for electrical resistance tomography

With the advantages of non-invasion, non-radiation, low-cost and high-speed, electrical resistance tomography (ERT) is suitable for online measurement of multiphase flow inside a pipeline. However, due to the highly non-linear and ill-posed characters of the inverse problem, its spatial resolution is low. As a kind of promising solutions for the issue, the iterative methods are always with low computational efficiency. In the paper, a Dimensional Reduction Simultaneous Iterative Reconstruction technique (DR-SIRT) is proposed to solve the two and three dimensional ERT problems. Scalar of the algebraic system for the solution of the inverse problem is significantly reduced by the orthogonal projection operator. The over-smoothing character of the iterative regularization method is mitigated by the non-negative constraint. The results from the numerical testes and the statistic experiments proved that the usage of the DR-SIRT can save the computational resources both in 2D and 3D cases. The discussions on the factors influencing the imaging speedy and quality, such as the number of electrodes and the truncation coefficient in the projection operator, provide some guidelines for the practical applications of the DR-SIRT algorithm.     

ECT图像重建正则化参数选取新方法

电容层析成像图像重建是一不适定反问题。此种情况下，仅使用最小二乘法不能保证获得满意的介质分布图像重建结果，因此广泛使用TIkhonov正则化算法来产生适当的解。正则化参数的合适选取对图像重建至关重要，其对重建质量和计算时间都有影响。本文提出了一种基于最平坦斜率的Tikhonov正则化参数选择方法，并针对2种典型介质分布，将基于此方法计算的正则化参数同L-曲线法在电容测量数据无噪声和施加噪声情况下的图像重建结果进行了比较。     

Dynamic imaging method using the low n-rank tensor for electrical capacitance tomography

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.     

Nonlinear image reconstruction using a GA-ECT technique in electrical capacitance tomography

Electrical capacitance tomography (ECT) is a non-invasive measurement technique that estimates the dielectric permittivity distribution of an inhomogeneous object from the boundary potentials at floating electrodes or mutual capacitances. In this paper, a stochastic inverse technique based on genetic algorithm (GA-ECT) is developed, which is adapted to the two different methods, i.e. potential measurement and capacitance measurement. Numerical simulation results are presented to evaluate the inverse technique both for noise free and noisy data and the results show that quantitative image can be reconstructed not only with the low permittivity contrast but also with the high contrast. Furthermore, the influence of a priori knowledge to image reconstruction is discussed.     

动态电容层析成像图像重建算法

提出了融合ECT测量信息和被测对象动态演化信息的新型图像重建模型;基于Tikhonov正则化方法,建立一个同时考虑了ECT测量信息、被测对象动态演化信息、时间与空间约束的新型图像重建目标泛涵,将图像重建问题转化为最优化问题;提出了集成分裂Bregman迭代法优势的新型算法求解该目标泛涵。数值仿真结果表明,所提出的图像重建算法其图像重建质量均优于OIOR算法、STR算法及PLI算法;同时由于所提出的图像重建算法同时考虑了测量数据和重建模型的不精确性,其抵抗测量噪声的能力得以提高。     

电容层析成像图像重建的总变差正则化算法

针对电容层析成像（ECT）逆问题解的不适定性,本文提出一种基于总变差（total variation,TV）正则化的图像重建算法。同传统的2范数Tikhonov正则化方法相比,该算法（基于1范数正则化）不仅保证了逆问题求解的稳定性,而且提高了对介质非连续分布的区域成像的分辨能力,具有良好的保边缘性。仿真及实验结果表明,该算法在重建图像质量和重建速度两方面均具有优势。     

Image reconstruction of electrical capacitance tomography based on optimal simulated annealing algorithm using orthogonal test method

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.