Optimal similarity norm for electrical tomography based on Bregman divergence

Abstract

Electrical Tomography (ET) is an advanced visualization technique, which can reconstruct all targets in an investigated field based on boundary measurements. Since the spatial resolution in the ET process can be greatly affected by the selected similarity norm, different norms may result in different ET time and spatial resolutions. In the tomographic applications nowadays, Bregman divergence (BD) has attracted increasing attention. BDs are a family of generalized similarity norm, and they can measure the similarity/difference between any two targets more accurately. Specifically, the mostly used similarity norm in the ET process (e.g., L2-norm) is only a special case of the BD family. As the key step of applying BD to the ET process, an execution method is proposed in this paper, together with the selection criteria for the optimal norm in the BD family. Simulations and experiments were conducted, and the results show that the use of an optimal BD can effectively improve the spatial resolution of an ET image.

Clustering-Based Component Fraction Estimation in Solid–Liquid Two-Phase Flow in Dredging Engineering

“…However, solid and liquid objects are dynamically changeable and it is difficult to satisfy the assumption. The ERT reconstruction of the detected field has natural limitations such as the ill-posed problem and ‘soft field’ effect [ 18 , 19 ]. These limitations make the CF estimation have uncertain, inconsistent, and incomplete characteristics.…”