Regularization iteration imaging algorithm for electrical capacitance tomography

Tong, Guowei; Shi, Liwei; Chen, Hongyan; Wang, Xueyao

doi:10.1088/1361-6501/aaa3c5

Cited by 18 publications

(5 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…from increasing to decreasing or from decreasing to increasing, the product of two adjacent capacitance residual norm differences is less than zero. Therefore, the iteration terminating condition can be described by equation (12). The maximum number of iterations n max is set to avoid too many iterations.…”

Section: Algorithmmentioning

confidence: 99%

“…For example, Ye et al [11] proposed an image reconstruction algorithm based on sparse representation to improve ECT image reconstruction quality. Tong et al [12] proposed a regularization iteration imaging algorithm for ECT with a new cost function considering the sparsity and low rank properties of the imaging targets simultaneously. Zhang et al [13] proposed an ECT image reconstruction algorithm based on Barzilai-Borwein gradient projection for sparse reconstruction algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…But, due to the complexity of ECT inverse problem, the regularization parameters determined by these methods are not necessarily the best choice. Hence, the regularization parameters are often chosen empirically [12]. Watzenig et al [21] proposed an adaptive regularization parameter adjustment method, in which a condition-number based estimate of the regularization parameter for the first iteration step is required to choose the weighting factor for adapting the regularization parameter iteratively.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An ECT image reconstruction algorithm based on object-and-background adaptive regularization

Yan

Wang

et al. 2020

Meas. Sci. Technol.

View full text Add to dashboard Cite

Electrical capacitance tomography (ECT) image reconstruction is an ill-posed inverse problem. Regularization methods are generally employed to solve the ill-posed problem, and the reconstructed image quality is seriously influenced by the selection of the regularization parameter. At present, a same regularization parameter is generally adopted for the whole reconstruction region, but the object region and the background region have different requirements on the regularization parameter, so it is difficult to obtain high-quality reconstruction images. In order to solve this problem, an image reconstruction algorithm based on object-and-background adaptive regularization, the OABAR algorithm for short, is proposed in this paper. The basic idea of the proposed OABAR algorithm is to dynamically divide the reconstructed region into object and background regions according to the reconstructed gray values. This is followed by iteratively reducing the regularization parameter values corresponding to the object region on the one hand to improve the detail reconstruction ability of the object region. On the other hand, a fixed regularization parameter value larger than the initial regularization parameter is provided for the background region to improve the smoothing ability of the background region. The iterative process is terminated when the change trend of capacitance residual norm first reverses or the number of iterations reaches the preset maximum value. If the change trend of capacitance residual norm does not reverse within the preset maximum number of iterations, the reconstructed result with the minimum capacitance residual norm is taken as the final result. Simulation and experimental tests were carried out and the results verify the effectiveness of the proposed algorithm on improving ECT image reconstruction quality.

show abstract

Section: Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An ECT image reconstruction algorithm based on object-and-background adaptive regularization

Yan

Wang

et al. 2020

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…This problem can be solved by the singular value threshold (SVT) method [43]. First, we give the definition of the soft shrinkage algorithm…”

Section: Optimization Frameworkmentioning

confidence: 99%

Nonlocal total variation based dynamic PET image reconstruction with low-rank constraints

Zhang

Liu

2019

Phys. Scr.

View full text Add to dashboard Cite

Faced with the low signal-to-noise ratio of measurements due to the short data acquisition time between adjacent frames, reconstruction techniques for dynamic positron emission tomography (dPET) images have been under development for decades. In this paper, a novel reconstruction method of dPET is presented. We first enforce a joint low-rank model to simultaneously exploit the spatiotemporal priors. Considering that PET images suffer from noise actually, a nonlocal total variation (TV) constraint is incorporated in the low-rank framework because it is able to preserve fine structures and remove the staircase effect by using image redundancies. We solve the objective function by using the augmented Lagrangian multiplier method with variable splitting. We then validate the effectiveness of the proposed method with Monte Carlo-simulated datasets and real patient data. The proposed approach gives superior reconstruction results compared with the maximum likelihoodexpectation maximization and low-rank TV methods.

show abstract

“…Besides, the proposed method showed better performance for multiple bleeding targets. Tong et al [15] proposed a new cost function, which considered both the sparsity and low-rank properties of the reconstructed objects. An iterative scheme was developed to solve the cost function based on the split Bregman and fast iterative shrinkage thresholding algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Sparsity Reconstruction Algorithm of Electromagnetic Tomography Technique for High Conductivity Medium Imaging

Liu

Wang

et al. 2023

Sens Imaging

View full text Add to dashboard Cite

Electromagnetic tomography (EMT) is a versatile tomographic imaging technique for reconstruction of conductivity and/or permeability distribution due to the advantages of non-contact, non-intrusive, lowcost, simple structure and fast imaging. However, the ill-posed and ill-conditioned features of EMT make it difficult to obtain high quality reconstructed images. To improve the spatial resolution of the high conductivity medium imaging, the L1-L1 framework objective function is presented, which uses L1 norm as both data fidelity term and regularization term to weaken the influence of the data outliers and impose the sparsity feature of the measured objects. An improved Split Bregman method is proposed to solve the complicated optimization problem efficiently, which splits it into several simple sub-tasks.Each subtask can be solved by adopting the proper method. Besides, an acceleration strategy is introduced to improve the convergence rate. Numerical simulations are used to verify the effectiveness and competitive performance of the proposed improved method. The experiments are carried out by the designed modularized EMT system to further verify the effectiveness of the proposed method. The reconstructed images can precisely show the number and positions of the measured objects.

show abstract

Regularization iteration imaging algorithm for electrical capacitance tomography

Cited by 18 publications

References 35 publications

An ECT image reconstruction algorithm based on object-and-background adaptive regularization

An ECT image reconstruction algorithm based on object-and-background adaptive regularization

Nonlocal total variation based dynamic PET image reconstruction with low-rank constraints

A Sparsity Reconstruction Algorithm of Electromagnetic Tomography Technique for High Conductivity Medium Imaging

Contact Info

Product

Resources

About