Fast Algorithms for Poisson Image Denoising Using Fractional-Order Total Variation
In this paper, a new Poisson image denoising model based on fractional-order total variation regularization is proposed. To obtain its global optimal solution, the augmented Lagrangian method, the Chambolle’s dual algorithm and the primal-dual algorithm are introduced. Experimental results are supplied to demonstrate the effectiveness and efficiency of the proposed algorithms for solving our proposed model, with comparison to the total variation Poisson image denoising model.
KeywordsPoisson denoising Fractional-order total variation Augmented Lagrangian method Dual algorithm Primal-dual algorithm
The research has been supported by the Science and Technology Project of Jiangxi Provincial Department of Education (GJJ161111, GJJ171015), the NNSF of China grants (61865012), the CSC (201708360066), and the NSF of Jiangxi Province (20161BAB202040, 20151BAB207010).
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