Nonlocal Low-Rank And Prior Image-Based Reconstruction In A Wavelet Tight Frame Using Limited-Angle Projection Data

Computed tomography (CT) reconstruction for limited-angle projection data is an ill-posed inverse problem, that often produces artifacts near the edges of an image. In this study, a hybrid minimization model based on a nonlocal low-rank approximation and a prior image in a wavelet tight framework is proposed to improve reconstructions from limited-angle projections. Low-frequency wavelet coefficients of the reconstructed image were estimated using a nonlocal low-rank approximation and the $l_{2}$ norm minimization was applied to the difference between the high-frequency components of a prior image and the reconstructed image. In addition, the alternative direction method of multipliers (ADMM) was used for alternately minimization to solve two regularization terms which produce the most parameters. Experimental results demonstrated that the proposed algorithm offers several advantages over conventional iterative reconstruction techniques, including faster convergence, suppression of limited-angle artifacts, noise reduction, and the preservation of edges and other image details. This study represents the first time that nonlocal low-rank prior information has been applied to limited-angle CT.