Limited-angle CT reconstruction via the L1/L2 minimization
This addresses the problem of image reconstruction in computed tomography under limited-angle scanning, which is incremental as it builds on existing optimization methods.
The paper tackled limited-angle CT reconstruction by minimizing the L1/L2 term on the gradient, resulting in significant improvements over state-of-the-art methods as demonstrated on synthetic and experimental datasets.
In this paper, we consider minimizing the L1/L2 term on the gradient for a limited-angle scanning problem in computed tomography (CT) reconstruction. We design a specific splitting framework for an unconstrained optimization model so that the alternating direction method of multipliers (ADMM) has guaranteed convergence under certain conditions. In addition, we incorporate a box constraint that is reasonable for imaging applications, and the convergence for the additional box constraint can also be established. Numerical results on both synthetic and experimental datasets demonstrate the effectiveness and efficiency of our proposed approaches, showing significant improvements over the state-of-the-art methods in the limited-angle CT reconstruction.