基于FCM及快速迭代收缩阈值算法的平面ECT图像重建OA北大核心CSTPCD

To improve the imaging accuracy of planar array capacitive imaging systems,a fast iterative shrinkage-thresholding algorithm(FISTA)based on fuzzy C-means clustering(FCM)for data optimization is proposed.According to the characteristics of planar array capacitance data,firstly,FCM algorithm is used to classify the measured capacitance values,preserve the effective capacitance values,and achieve dimensionality reduction of the capacitance vector.Then,discrete wavelet bases(DWT)are used to sparsely represent gray values,and L1 regularization model is established to solve the problem using FISTA to achieve image reconstruction.Finally,the capacitance values processed by FCM are used for reconstruction comparison with Landweber algorithm and Tikhonov algorithm respectively.The simulation and experimental results show that the average relative error of the reconstructed image using the proposed algorithm is about 0.0527,and the average correlation coefficient is about 0.9422,both of which are superior to other algorithms.Moreover,the reconstructed image has fewer artifacts and is closer to the real situation.Therefore,the proposed algorithm has better reconstruction performance.