计量学报2024，Vol.45Issue(8)：1132-1138,7.DOI:10.3969/j.issn.1000-1158.2024.08.07
联合改进稀疏正则化ECT图像重建算法
Jointly Improved Sparse Regularization ECT Image Reconstruction Algorithm
摘要
Abstract
To improve the ill-conditioned and ill-posed problem in the inverse problem solving process of electrical capacitance tomography(ECT),a jointly improved sparse regularization image reconstruction algorithm is proposed.Firstly,the sensitivity matrix is optimally preprocessed by the adaptive truncated singular value algorithm to eliminate the redundant information in the matrix.Secondly,in order to enhance the sparsity and stability of the solution,the L1-αL2 sparse regularization is jointly improved based on the optimized sensitivity matrix to construct new convex function terms.Finally,the solution is performed by the fast iterative threshold shrinkage algorithm to accelerate the iterative convergence speed.The improved algorithm achieves an average correlation coefficient of 0.881 3 in the reconstructed image,the image error is reduced to 0.211 1 on average,and the imaging speed is kept within 0.10 s.The simulation and experimental results show that the improved algorithm improves the ill-posed and ill-condition degree and enhances the image reconstruction accuracy while having strong robustness and real-time performance.关键词
多相流测量/电容层析技术/自适应截断奇异值/稀疏正则化/图像重建Key words
multiphase flow measurement/electrical capacitance tomography/adaptive truncated singular value/sparse regularization/image reconstruction分类
通用工业技术引用本文复制引用
马敏,孙妮..联合改进稀疏正则化ECT图像重建算法[J].计量学报,2024,45(8):1132-1138,7.基金项目
国家自然科学基金(61871379):天津市教委科研计划(2020KJ012) （61871379）
