3D Reconstruction in Planar Array Electrical Capacitance Tomography Based on Depth Estimation and Sparse Representation

Planar array electrical capacitance tomography (ECT) has an attractive prospect for underground detection of non-metallic materials, e.g., landmine and subsurface cavity detection. Accurate 3D image reconstruction is essential to these applications. This paper presents a 3D reconstruction method based on depth estimation and sparse representation. The approximate depth of object is attained by postprocessing an image by Tikhonov method and regarded as the prior knowledge for the sparse representation of target distribution. The 3D reconstruction is achieved by solving a dedicated optimization problem using the Gauss-Newton method. To simulate the application of subsurface cavity detection, a planar array ECT model has been established and solved numerically. With five targets of various shapes and depths, the estimated depths have been calculated and compared. Those five targets various shapes are then reconstructed using the proposed method and conventional methods, with their performances evaluated and compared. The preliminary results validated the superior performance of the proposed method.