Low-dose X-ray Computed Tomography Image Reconstruction Using Edge Sparsity Regularization

Abstract

Total variation (TV) regularization is one of popular techniques for low dose x-ray computed tomography image reconstruction. However, the reconstruction image by TV method often suffers staircase effect. In this paper, we propose an edge sparsity model, which penalizes the difference between L1 norm and L2 norm of gradient, for low dose x-ray computed tomography image reconstruction. Alternating direction method of multipliers (ADMM) is adopted to solve the proposed model. Experiment results on simulation data and real data are presented to verify the effectiveness of the proposed method.