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Volume 7, Issue 4, 1 August 2023, Pages 563-580
Abstract. This paper proposes a novel nonconvex regularization functional by using an adaptively weighted difference model of anisotropic and isotropic total variation. By choosing the weights adaptively at each pixel, our model can enhance the anisotropic diffusion so as to achieve robust image recovery. Regarding to numerical implementations, we express the proposed model into a saddle point problem with the help of a dual formulation of the total variation, followed by a primal dual method to find a model solution. Numerical experiments demonstrate that the proposed approach is superior over several gradient-based methods for image denoising in terms of both visual appearance and quantitative metrics of signal noise ratio (SNR) and structural similarity index measure (SSIM).
How to Cite this Article:
B. Shi, M. Li, Y. Lou, Adaptively weighted difference model of anisotropic and isotropic total variation for image denoising, J. Nonlinear Var. Anal. 7 (2023), 563-580.