  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.
