A Patch-Based Transformer Method for Electrical Capacitance Tomography Image Reconstruction

Abstract

Electrical capacitance tomography (ECT) is a contactless and non-invasive imaging technique, which visualizes the internal permittivity distribution around a region utilizing boundary capacitance measurements. It has been widely used in the fields of object classification, tactile sensing and multiphase flows monitoring. However, due to the inherent nonlinearity and ill-conditioned nature of the ECT inverse problem, its practical implementation remains limited by challenges in the image reconstruction accuracy. To tackle the above problems, we propose a patch-based transformer method (PT) for an accurate reconstruction of ECT images. Specifically, the complex capacitance-to-image mapping is systematically decoupled into the capacitance-to-patch feature extraction and patch-to-image reconstruction, enabling more efficient and accurate permittivity distribution recovery through localized feature learning and global context integration. Additionally, a simulation ECT dataset for objects with varying sizes and positions is established.