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.
@article{arxiv.2006.00601,
title = {Limited-angle CT reconstruction via the L1/L2 minimization},
author = {Chao Wang and Min Tao and James Nagy and Yifei Lou},
journal= {arXiv preprint arXiv:2006.00601},
year = {2021}
}