![]() ![]() T1 - Fast local image inpainting based on the Allen-Cahn model To demonstrate the robustness and accuracy of the proposed method, various numerical results on real and synthetic images are presented.", We prove the unconditional stability of the proposed scheme. The linear equation is discretized by using a fully implicit scheme and the nonlinear equation is solved analytically. We split the governing equation into one linear equation and one nonlinear equation by using an operator splitting technique. Thus the proposed method is computationally efficient. The second feature is that the pixel values outside of the inpainting region are the same as those in the original input image since we do not compute the outside of the inpainting region. The first feature is that the pixel values in the inpainting domain are obtained by curvature-driven diffusions and utilizing the image information from the outside of the inpainting region. The proposed algorithm is applied only on the inpainting domain and has two features. To demonstrate the robustness and accuracy of the proposed method, various numerical results on real and synthetic images are presented.Ībstract = "In this paper, we propose a fast local image inpainting algorithm based on the Allen-Cahn model. In this paper, we propose a fast local image inpainting algorithm based on the Allen-Cahn model. ![]()
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