联合改进稀疏正则化ECT图像重建算法OA北大核心CSTPCD

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.