Numer. Math. Theor. Meth. Appl., 15 (2022), pp. 415-441.
Published online: 2022-03
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In this paper, we propose new algorithms for multiplicative noise removal based on the Aubert-Aujol (AA) model. By introducing a constraint from the forward model with an auxiliary variable for the noise, the NEMA (short for Noise Estimate based Multiplicative noise removal by alternating direction method of multipliers (ADMM)) is firstly given. To further reduce the computational cost, an additional proximal term is considered for the subproblem with regard to the original variable, the NEMA$_f$ (short for a variant of NEMA with fully splitting form) is further proposed. We conduct numerous experiments to show the convergence and performance of the proposed algorithms. Namely, the restoration results by the proposed algorithms are better in terms of SNRs for image deblurring than other compared methods including two popular algorithms for AA model and three algorithms of its convex variants.
}, issn = {2079-7338}, doi = {https://doi.org/10.4208/nmtma.OA-2021-0134}, url = {http://global-sci.org/intro/article_detail/nmtma/20358.html} }In this paper, we propose new algorithms for multiplicative noise removal based on the Aubert-Aujol (AA) model. By introducing a constraint from the forward model with an auxiliary variable for the noise, the NEMA (short for Noise Estimate based Multiplicative noise removal by alternating direction method of multipliers (ADMM)) is firstly given. To further reduce the computational cost, an additional proximal term is considered for the subproblem with regard to the original variable, the NEMA$_f$ (short for a variant of NEMA with fully splitting form) is further proposed. We conduct numerous experiments to show the convergence and performance of the proposed algorithms. Namely, the restoration results by the proposed algorithms are better in terms of SNRs for image deblurring than other compared methods including two popular algorithms for AA model and three algorithms of its convex variants.