An Intrinsic Regression-Based Method for Eliminating Complex Ring Artifacts in CT Images

X-ray computed tomography (CT) systems are inherently susceptible to ring artifacts, primarily caused by hardware imperfections such as inconsistent detector responses, uncertainties in X-ray source intensity, and variations in the energy thresholds of detector pixels, etc. These artifacts degrade CT images in more complex ways than imagined, thereby having negative impacts on subsequent analysis. In this paper, we propose P-LIWLR, an effective method for ring artifact removal that compensates for system deficiencies. The proposed method demonstrates strong robustness against diverse ring artifact patterns. Our approach is motivated by the discontinuity of stripe artifacts in the sinogram domain and the geometric properties of the ideal mean projection (MP). By leveraging these characteristics, unstable ring artifacts are decomposed into stable subtypes and separately eliminated via local MP restoration using a novel regression model, LIWLR. Within LIWLR, an intrinsic weighting mechanism is introduced to automatically distinguish and prioritize reliable data over corrupted samples, thereby ensuring the effectiveness of the regression results. Experimental results demonstrate that the proposed method significantly outperforms state-of-the-art ring artifact removal techniques, both in terms of artifact suppression and accurate restoration of critical image structures.