A Fractional-Order Primal-Dual Denoising Algorithm

Objective: By combining fractional calculus and duality theory, a novel fractional-order primal-dual model which is equivalent with the fractional ROF model is proposed. We theoretically analyze its structural similarity with the saddle-point optimization model. So the algorithms for solving the saddle-point problem can be used for solving the model. Methods: The primal-dual algorithm based on resolvent for solving the saddle-point problem is used for solving the proposed model. The adaptive variable step size iterative optimization strategy is used, which can improve the optimizing efficiency, and remedy the step size limitation of the traditional numerical algorithms. In order to guarantee the convergence of the algorithm, the range of the parameter is given. Results: The experiment results show that the proposed fractional-order primal-dual model is effective in avoiding the staircase effect and preserving texture and detail information, and the adoptive numerical algorithm has faster convergence speed. Conclusion: This paper proposes a fractional-order primal-dual denoising model, which can be solved by a primal-dual algorithm based on resolvent. The experiment results show that the proposed model can improve the image visual effect effectively, and the adoptive numerical algorithm has faster convergence speed. Keywordsimage denoising; fractional-order; primaldual; saddle-point problem; variation method