Gas–liquid two-phase flows are encountered in various industrial processes involving high temperatures and high pressures, which necessitates nonintrusive sensing for real-time imaging of phase distribution and flow parameters. In this context, this article presents an electrical impedance tomography (EIT)-based eigenvalue correlation method that allows extracting two-phase flow features, namely, the void fraction and the flow regime, which are used in turn to improve flow visualizations. Benefiting from the so-called full-scan excitation strategy, the eigenvalue correlation method has been devised in to estimate the phase fraction from EIT raw measurements. In this article, this method is refined and integrated into an image-enhancing procedure, which is illustrated and validated using dynamic experimental data. A total of 80 experiments are considered with water and air mass flow rates ranging from 1.58 to 79.43 kg/min and from 0.1 to 5.0 kg/min, respectively, covering slug, plug, stratified smooth, stratified wavy, and annular flows. Based on a preliminary system calibration and a raw image guess, the volume-averaged void fractions are then estimated using the proposed method and integrated into EIT-based images to form binarized tomograms relative to the acquisition time. The EIT tomograms, thus, obtained show an excellent agreement with some γ-ray reference measurements of the phase distribution.