Split Bregmanized anisotropic total variation model for image deblurring

• An anisotropic total variation based model is proposed for image deblurring.
• Split Bregman iteration is used to solve the proposed minimization problem.
• The recovered images has more textures and less stair-casing effect.
• The proposed method is robust to different degree of blur kernels.
• The proposed method is robust to different degree of Gaussian noise.

In this paper, an effective image deblurring model is proposed to preserve sharp image edges by suppressing the stair-casing arising in the total variation (TV) based method by using the anisotropic total variation. To solve the difficult L1 norm problems, the split Bregman iteration is employed. Several synthetic degraded images are used for experiments. Comparison results are also made with total variation and nonlocal total variation based method. Experimental results show that the proposed method not only is robust to noise and different blur kernels, but also performs well on blurring images with more detailed textures, and the stair-casing effect is well suppressed.