Perona-Malik Diffusion-Driven Regularization for Image Resolution Enhancement in Electrical Capacitance Tomography

Abstract

Electrical Capacitance Tomography (ECT) is a non-invasive promising method for monitoring industrial processes, such as oil-gas flow in pipelines and solid-gas flow in pneumatic systems. Despite its potential benefits, ECT generates poor-quality images, often used only for qualitative analysis. A non-linear relationship between measured capacitances and permittivity distribution and the ill-posedness of the sensitivity matrix elements causes this limitation. This hinders the applicability of ECT in monitoring online industrial process applications. This work proposes a reconstruction method based on a nonlinear diffusion function to generate high-quality images from the measured capacitance data from the ECT system. The diffusion regularization functional helps to remove noise and preserve semantic features. Experimental results reveal that the proposed method generates high-quality and visually appealing images with a 15% reduction in distribution error and a 10% increase in correlation coefficient compared to state-of-the-art methods such as linear back projection and projected landweber. This allows further investigation into how nonlinear anisotropic diffusion can improve the applicability of ECT systems in industrial control and monitoring.