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Nonlinear multigrid method for solving the anisotropic image denoising models

机译:求解各向异性图像降噪模型的非线性多重网格方法

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摘要

In this paper, we study a nonlinear multigrid method for solving a general image denoising model with two L 1-regularization terms. Different from the previous studies, we give a simpler derivation of the dual formulation of the general model by augmented Lagrangian method. In order to improve the convergence rate of the proposed multigrid method, an improved dual iteration is proposed as its smoother. Furthermore, we apply the proposed method to the anisotropic ROF model and the anisotropic LLT model. We also give the local Fourier analysis (LFAs) of the Chambolle’s dual iterations and a modified smoother for solving these two models, respectively. Numerical results illustrate the efficiency of the proposed method and indicate that such a multigrid method is more suitable to deal with large-sized images.
机译:在本文中,我们研究了一种非线性多网格方法,用于求解具有两个L 1正则化项的通用图像降噪模型。与先前的研究不同,我们通过增强的拉格朗日方法更简单地推导了一般模型的对偶公式。为了提高所提出的多网格方法的收敛速度,提出了一种改进的对偶迭代算法,使其更平滑。此外,我们将提出的方法应用于各向异性ROF模型和各向异性LLT模型。我们还给出了Chambolle的双重迭代的局部傅里叶分析(LFA),以及分别用于解决这两个模型的改进的平滑器。数值结果说明了该方法的有效性,并表明这种多网格方法更适合处理大尺寸图像。

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