Adaptive fractional-order total variation image restoration with split Bregman iteration

Alleviating the staircase artifacts for variation method and adjusting the regularization parameters adaptively with the characteristics of different regions are two main issues in image restoration regularization process. An adaptive fractional-order total variation l₁ regularization (AFOTV-l₁) model is proposed, which is resolved by using split Bregman iteration algorithm (SBI) for image estimation. An improved fractional-order differential kernel mask (IFODKM) with an extended degree of freedom (DOF) is proposed, which can preserve more image details and effectively avoid the staircase artifact. With the SBI algorithm adopted in this paper, fast convergence and small errors are achieved. Moreover, a novel regularization parameters adaptive strategy is given. Experimental results, by using the standard image library (SIL), the lung imaging database consortium and image database resource initiative (LIDC-IDRI), demonstrate that the proposed methods have better approximation, robustness and fast convergence performances for image restoration.