一个基于1范数的电容层析成像图像重建迭代算法

电容层析成像因具有快速、廉价、非侵入传感等优点而被认为是具有广阔发展前景的过程成像技术.电容层析成像图像重建是一个典型的病态问题,它的解是不稳定的.为了获得有意义的重建结果,能够保证解的稳定性而又能提高重建图像质量的方法应该被采用.本文提出了一个基于1范数稳定的电容层析成像图像重建算法.将图像重建问题转化为一个最优化问题,并对目标泛函采用光滑逼近.在此基础上用Newton法求解该目标泛函.数值实验表明该算法是有效的,能够有效克服ECT图像重建的数值不稳定性,就本文所考察的重建对象而言,该法所获得的重建图像的质量有了一定的提高,从而为ECT图像重建引入了一种有效的方法.     

基于预优化变换的电容层析成像图像重建算法

本文提出了一种新型快速电容层析成像的图像重建算法。该方法首先将图像重建的病态问题转化为泛函极小化问题，然后采用Fletcher-Reeves算法（FR）有效地求解该泛函，并在求解的过程中引入物理意义上的约束使获得的解更符合实际情况。数值实验表明该算法的图像重建效果优于线性反投影法、Tikhonov正则法和Landweber迭代法，而且该算法具有较强的抗噪声能力。     

一个基于Quantile估计的电容层析成像图像重建算法

电容层析成像图像重建是一个典型的病态问题,它的解是不稳定的。为了获得有意义的重建结果,能够保证解的稳定性而又能提高重建图像质量的方法应该被采用。本文提出了一个新的电容层析成像图像重建算法。在分析标准Tikhonov正则法的基础上,针对ECT逆问题的病态特点利用Quantile估计和加权l_p范数构建扩展的目标泛函,将图像重建问题转化为一个最优化问题;在此基础上用Newton法求解该泛函。数值实验表明该算法是可行的,能够有效克服ECT图像重建的数值不稳定性。就本文所考察的重建对象而言,该法所重建图像的空间分辨率得到了提高。而且该算法计算直接、无需任何复杂的技巧,从而为ECT图像重建提供了一种有效的方法。     

视频图像中运动目标检测的快速方法

研究了基于Kalman滤波理论的渐消记忆递归最小二乘法,探讨了渐消记忆递归最小二乘法在图像序列重建图像背景中的应用,还提出两种检测移动阴影的快速算法,并用于视频图像运动目标的检测.先用背景重建算法对复杂背景进行更新预测,再用当前帧图像与预测背景差分提取动目标,最后检测并去掉目标的阴影.这里提出的检测方法简单、有效,便于用硬件快速实现.文中给出了实验结果.     

基于LU分解的共轭梯度法单步成像算法

单步和迭代电容层析成像（ECT）图像重建算法分别具有成像质量差和成像速度慢的缺点,为了快速得到高质量的重建图像,本文提出了一种新型的基于LU分解的单步共轭梯度成像法。该方法首先将ECT物理模型进行规范化和Tikhonov正则化处理,进而将LU分解的思想引入ECT方程的求解中,从而实现了单步图像重建。理论分析表明,该算法具有良好的单步收敛性。通过典型流型的仿真实验,证明了该算法可以获得优于反投影算法的重建图像。     

粒子滤波算法在ECT图像重建中的应用

针对电容层析成像技术(ECT)的图像重建质量精度较低的问题,提出了一种基于粒子滤波的ECT图像重建方法。首先,分析了ECT图像重建基本原理,以系统状态估计的方式描述了ECT图像重建最优解的搜索过程,并建立了状态空间模型。然后,以线性反投影(LBP)算法的图像重建结果作为初始状态,利用测量信息对从状态空间中获取的随机样本进行最优加权,以获得重建图像的最小方差估计。最后,对5种不同的流型进行了仿真实验。实验结果表明,利用本文方法获得的重建图像误差平均值为42.93%,相关系数平均值为0.813 9,比LBP算法、Landweber迭代算法和IMN-SNOF算法得到的相应指标要好。本文方法是一种有效、精度较高的ECT图像重建方法,为ECT图像重建技术提供了新的途径和手段。     

Application of particle filtering algorithm to image reconstruction of ECT

针对电容层析成像技术(ECT)的图像重建质量精度较低的问题,提出了一种基于粒子滤波的ECT图像重建方法.首先,分析了ECT图像重建基本原理,以系统状态估计的方式描述了ECT图像重建最优解的搜索过程,并建立了状态空间模型.然后,以线性反投影( LBP)算法的图像重建结果作为初始状态,利用测量信息对从状态空间中获取的随机样本进行最优加权,以获得重建图像的最小方差估计.最后,对5种不同的流型进行了仿真实验.实验结果表明,利用本文方法获得的重建图像误差平均值为42.93％,相关系数平均值为0.813 9,比LBP算法、Landweber迭代算法和IMN-SNOF算法得到的相应指标要好.本文方法是一种有效、精度较高的ECT图像重建方法,为ECT图像重建技术提供了新的途径和手段.     

基于聚类电阻层析成像静态图像重建算法

为了提高静态图像重建算法求解电阻层析成像逆问题的实时性与精度,提出一种基于敏感场区域内三角形有限元聚类的静态图像重建算法。新算法初期采用改进牛顿-拉夫逊算法,并根据算法振荡次数超过允许最大值时所对应的最优图像重建结果,对敏感场区域内所有三角形有限元进行聚类,最后利用改进粒子群算法求解电阻层析成像逆问题,从而实现静态图像重建。新算法每隔一定迭代次数对敏感场区域内所有三角形有限元进行重新聚类,以减小粒子维数,提高新算法求解电阻层析成像逆问题的精度。实验结果表明,相比改进牛顿-拉夫逊算法及组合算法,新算法效果最理想。     

基于ART的光学过程层析成像扇束图像重建

光学过程层析成像是近几年发展起来的一种新型的光学测量技术，它源于医学CT的基本理论和方法，可认为是医学CT技术在工业过程监测领域的自然延伸和发展。然而，由于过程层析成像的被测对象为快速变化的工业过程，因此，其投影数据的数量比医学CT少得多，而实时性则要求更高。利用一种扇束扫描制式的光学传感器有利于提高光学过程层析成像的空间分辨率及测量精度，但在这种扫描制式下，引用医学CT的常见图像重建算法(如数据重排方法、反投影法和滤波反投影法等)却不适用或难以胜任工业检测的要求。为此提出了一种代数重建技术来提高光学过程层析成像的测量精度和速度。该算法不仅适用于少数投影数据的情况，也能使求解过程遍历几乎所有的图像像元，因此成像效果好和实时性较高，具有工程应用价值。     

基于改进正则法的ECT图像重建算法

电容层析成像图像重建是一个典型的病态问题,其解是不稳定的。为获得良好的重建效果,需要采用既保证解的稳定性且又能提高重建图像质量的算法。本文提出了一种新的图像重建算法。在分析标准Tikhonov正则法的基础上,针对ECT逆问题的病态性进行改进,并推导出两步图像重建算法：第一步利用标准Tikhonov正则法的计算值获得权矩阵的估计;第二步采用本文所推导的改进Tikhonov正则法获得最终的重建图像。数值实验表明,该算法所获得的图像重建质量得到了明显的提高,且该算法无需迭代,保证了算法实时性。     

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).     

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.     

An extreme learning machine combined with Landweber iteration algorithm for the inverse problem of electrical capacitance tomography

The image reconstruction of the electrical capacitance tomography (ECT) is an ill-posed and sparse problem. In order to increase the accuracy and speed of the image reconstruction, this paper proposes a new reconstruction algorithm which is based on the extreme learning machine (ELM) with the Landweber iteration method. Firstly, a nonlinear mapping model is established between the pixel gray-scale values and the interelectrode capacitances by using the ELM which has a good learning ability and high speed. Secondly, the Landweber iteration method, which has a good performance in convergence and stability, is applied to calculate the output weight matrix of ELM. Finally, a convergence and stable mapping model of ELM with the Landweber iteration algorithm (L-ELM) for ECT image reconstruction is trained on Matlab platform. Both simulation and measurement tests are carried out to evaluate and analyze the proposed method. Experimental results indicate that the proposed algorithm has good generalization ability and high image reconstruction quality which are better than those of conventional ELM algorithm.     

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。     

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.     

Preconditioned Landweber iteration algorithm for electrical capacitance tomography

Electrical capacitance tomography (ECT) has been used to obtain the cross-section images of processes with different dielectric materials inside. Image reconstruction with ECT is to retrieve the permittivity distribution of materials inside the sensor from the capacitance measurements. Algorithms for ECT image reconstruction should be both precise and fast in order to satisfy the requirements of real-time monitoring of the dynamic behavior of processes. Several algorithms have been developed for ECT image reconstruction. The Landweber iteration is the most precise one. However, the low convergence rate of the Landweber iteration limits its application for on-line imaging.This paper introduces an iterative algorithm based on the Landweber method with preconditioning for ECT image reconstruction. A preconditioner, which is equivalent to a filter, is applied to the Landweber iteration. The convergence of this algorithm is analyzed. Its performance is evaluated by using simulated and experimental data corresponding to certain typical permittivity distributions. Preliminary numerical and experimental results show that this algorithm converges more rapidly than the Landweber iteration without preconditioning. Therefore, image reconstruction iteration can be accelerated, which makes on-line quantitative image reconstruction possible.     

基于自适应模拟退火及 LM 联合反演算法的 ECT 图像重建

为解决电容层析成像技术(ECT)中图像重建的非线性和病态性问题,提出了一种自适应模拟退火-Levenberg Marquardt (ASA-LM)联合反演算法。 改进了标准模拟退火(SA)算法的新解生成策略、能量函数的定义及退火策略,并结合 LM 的直接局 部搜索方法联合反演 ECT 图像重建问题。 同时,利用 Savitzky-Golay (SG) 滤波对 ECT 图像重建所需电容数据进行平滑处理以 提高其信噪比。 最后,进行仿真及静态实验,并与线性反投影(LBP)、Landweber 迭代及标准 SA 算法进行了比较。 结果表明,与 其他 3 种算法相比,ASA-LM 算法收敛速度快、图像重建质量明显提高,边缘信息保真度高,重建图像的平均相对误差为 0. 331 1,平均相关系数为 0. 933 1。     

3D ECT reconstruction by an improved Landweber iteration algorithm

Electrical capacitance tomography (ECT) is a relatively mature non-invasive imaging technique that attempts to map dielectric permittivity of materials. Recently, 3D ECT has gained interest because of its potential to generate volumetric images. The study of a fast and accurate image reconstruction algorithm is a challenge task, especially for 3D reconstruction. In this paper, we propose an improved Landweber iteration algorithm. We incorporate an additional acceleration term into the cost function and apply an adaptive threshold operation to the image obtained in each iteration for reducing artefacts. The algorithm proposed is tested by the noise-free and noise-contaminated capacitance data. Sensitivity matrixes and capacitance data of a 3D ECT sensor are obtained by using the finite element (FE) method. Extensive simulations in 3D reconstruction are carried out. The results verify the effectiveness of these improvements. Both the reconstruction time and the artefacts in the reconstructed image are reduced obviously. The experimental results of 3D reconstruction of objects in the shape of letters U and L confirm the effectiveness of the proposed algorithm further.