Towards industrially relevant total variation based reconstruction of few-view computed tomography by exploiting noise level estimation

Abstract

To extend the applicability of in-line computed tomography (CT) within Industry 4.0, accelerating the data acquisition and image reconstruction process is essential to meet the demands of real-time, high-throughput inspection. This paper focuses on accelerating in-line CT by addressing the trade-off between image quality and angular sampling reduction through the development of dedicated reconstruction algorithms. Various inherent properties of in-line CT are leveraged as a priori knowledge, specifically the noise level, within a total variation (TV)-based reconstruction framework to enhance reconstruction quality, support automation, and enable accurate image analysis using 2%–5% of the data required by standard methods such as Feldkamp–Davis–Kress (FDK).