Limited-angle CT reconstruction via the L1/L2 minimization

TL;DR

This paper introduces a novel L1/L2 gradient minimization approach for limited-angle CT reconstruction, employing a convergent ADMM framework with box constraints, leading to improved results over existing methods.

Contribution

The paper develops a specific ADMM-based optimization framework with convergence guarantees for limited-angle CT reconstruction using L1/L2 gradient minimization, incorporating box constraints.

Findings

Significant improvement over state-of-the-art methods.

Effective convergence of the proposed ADMM approach.

Demonstrated robustness on synthetic and real datasets.

Abstract

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.