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- W2371297981 abstract "Real images usually have two layers,namely,cartoons and textures,both of these layers have sparse approximations under some tight frame systems such as curvelet,local DCTs,and B-spline wavelet.In this paper,we solve the image inpainting problem by using two separate tight frame systems which can sparsely represent the two parts of the image.Different from existing schemes in the literature which are either analysis-based or synthesis-based sparsity priors,our minimization formulation applies the nonconvex sparsity prior via the balanced approach.We also derive iterative algorithms for finding their solutions.Numerical simulation examples are given to demonstrate that our proposed nonconvex method achieves significant improvements over the classical l1 sparse method and the variation TV method in image inpainting." @default.
- W2371297981 created "2016-06-24" @default.
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- W2371297981 date "2014-01-01" @default.
- W2371297981 modified "2023-09-26" @default.
- W2371297981 title "Nonconvex image inpainting via balanced regularization approach" @default.
- W2371297981 hasPublicationYear "2014" @default.
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