Image reconstruction from few views by L0-norm optimization

In the medical computer tomography (CT) field, total variation (TV), which is the 1  -norm of the discrete gradient transform (DGT), is widely used as regularization based on the comprehensive sensing (CS) theory. To overcome the TV model’s disadvantageous tendency of uniformly penalize the image gradient and over smooth the low-contrast structures, an iterative algorithm based on the 0  -norm optimization of the DGT is proposed. To rise to the challenges introduced by the 0  -norm DGT, the algorithm uses a pseudo-inverse transform of DGT and adapts an iterative hard thresholding (IHT) algorithm, whose convergence and effective efficiency have been theoretically proven. The simulation demonstrates our conclusions and indicates that the algorithm proposed in this paper can obviously improve the reconstruction quality.