To handle the large-scale optimization problem caused by regular image restoration

this paper introduces a novel image restoration method based on Bregman iterative double regularization. In this method

the designed objective function considers both the total variation regularization and the wavelet domain sparsity constraint

and solves the problem under Bregman framework with the split Bregman iterative algorithm. The algorithm converts the complex optimization problem to several iterations

each of which requires only several simple Fast Fourier Transformations and shrinkage operations. The experimental results show that the proposed method improves both the objective SNR and the subjective perceptual image quality with a faster convergence rate compared to existing approaches.