Image Reconstruction of Planar Electrical Capacitance Tomography Based on DBSCAN and Self-Adaptive ADMM Algorithm

Planar electrical capacitance tomography (ECT) has broad application prospects in the defect detection of composite materials. The image reconstruction of planar ECT is a nonlinear and ill-posed inverse problem, which limits the accuracy of image reconstruction. An image reconstruction method based on the density-based spatial clustering of applications with noise cluster (DBSCAN) and self-adaptive alternating direction method of multipliers (SADMM) algorithms is proposed in this paper. Firstly, the characteristic capacitance values are extracted from the independent measurement capacitance values through the DBSCAN algorithm. Then, the original inverse problem is transformed into a set of local optimization sub-problems, and the SADMM algorithm with an adaptive penalty parameter is used to solve these subproblems to reconstruct the image. The experimental results show that the average relative error of the reconstructed images obtained by this algorithm is 0.086, which is significantly lower than other algorithms, the presented algorithm has higher anti-noise performance and can provide more accurate reconstructed images.