No abstract

No abstract

No abstract

No abstract

No abstract

Decision-makers should consider the baseline conditions of medical quality and the practice size before new medical policies are enacted. Furthermore, most studies are retrospective and observational with high level of heterogeneity though, the descriptive analysis is still of significance.

No abstract

Multiplicative noise removal problem has received considerable attention in recent years. The total variation regularization method for the solution of the noise removal problem can preserve edges well but has the sometimes undesirable staircase effect. In this paper, we propose a fast high-order total variation minimization method to restore multiplicative noisy images. The proposed method is able to preserve edges and at the same time avoid the staircase effect in the smooth regions. An alternating minimization algorithm is employed to solve the proposed high-order total variation minimization problem. We discuss the convergence of the alternating minimization algorithm. Some numerical results show that the proposed method gives restored images of higher quality than some existing multiplicative noise removal methods.