CT Image Reconstruction via Nonlocal Low-Rank Regularization and Data-Driven Tight Frame

X-ray computed tomography (CT) is widely used in medical applications, where many efforts have been made for decades to eliminate artifacts caused by incomplete projection. In this paper, we propose a new CT image reconstruction model based on nonlocal low-rank regularity and data-driven tight frame (NLR-DDTF). Unlike the Spatial-Radon domain regularization, proposed NLR-DDTF uses an asymmetric treatment Radon inpainting, which combines approximation method spatial frame-based regularization inpainting. An alternative direction minimization algorithm designed solve model. Several numerical experiments comparisons are provided illustrate superior performance of method.