Hybrid data fidelity, weighted difference of TV norms, model and algorithm for noisy image deblurring

Although difference of convex model has attracted many research efforts due to its superior performance for image processing, no attention has focused on robust data fidelity for this model. In this paper, we propose a novel model, which combines the l1 and l2 fidelity terms with a weighted difference of anisotropic and isotropic total variation (TV). Since our model takes a new difference form of convex terms, we employ difference of convex algorithm (DCA). In this paper, we adopt the augmented Lagrangian method (ALM) to solve each DCA subproblem of the proposed model, which is named as DCAALM. Experimental results on image deblurring demonstrate that the proposed methods outperform other competing methods in terms of quantitative criteria and perceptual quality.