A hybrid TV-KF slurry level impedance imaging method combining total variation regularization and Kalman filtering is proposed to address the challenges of accurate detection of slurry level, low visualization level, and overly smooth imaging results in flotation processes. Firstly, a field model for measuring the flotation slurry level is constructed to obtain the boundary voltage of the field, and a flotation process error function based on total variation regularization is established to calculate the initial conductivity value of the slurry level. Secondly, based on the total variation regularization algorithm, the conductivity value is calculated as prior information for the prediction equation of the Kalman filter algorithm. The updated equation and prediction equation of the Kalman filter algorithm are iteratively updated using the measured voltage values over time. Finally, based on the proposed TV-KF impedance imaging algorithm, the conductivity distribution of the slurry level is solved to obtain accurate slurry level detection results. The simulation and experimental results show that the proposed algorithm has higher resolution and better edge characteristics of the interface between slurry and froth, providing more comprehensive and accurate slurry level information. In various slurry level simulation models, the Pearson correlation coefficient (PCC) exceeds 85%, while the image reconstruction error (IRE) is lower compared to other algorithms, resulting in better reconstruction performance. The maximum measurement error of the slurry level on the on-site experimental platform is less than 2.4 cm, meeting the accurate detection requirements of the flotation industry′s on-site liquid level. Compared with the existing methods, the proposed algorithm exhibits stronger visualization of froth layer information, better adaptability to slurry variations, higher sensitivity to froth fluctuations, and sustained, stable measurement of slurry levels, making it highly valuable for practical flotation applications.