Image Recovery via Nonlocal Operators
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TLDR
Two nonlocal regularizations for image recovery, which exploit the spatial interactions in images, are considered, which get superior results using preprocessed data as input for the weighted functionals.Abstract:
This paper considers two nonlocal regularizations for image recovery, which exploit the spatial interactions in images. We get superior results using preprocessed data as input for the weighted functionals. Applications discussed include image deconvolution and tomographic reconstruction. The numerical results show our method outperforms some previous ones.read more
Citations
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Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction
TL;DR: The proposed general algorithm framework for inverse problem regularization with a single forward-backward operator step, namely, Bregmanized operator splitting (BOS), converges without fully solving the subproblems, and numerical results on deconvolution and compressive sensing illustrate the performance of nonlocal total variation regularization under the proposed algorithm framework.
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Analysis and Approximation of Nonlocal Diffusion Problems with Volume Constraints
TL;DR: It is shown that fractional Laplacian and fractional derivative models for anomalous diffusion are special cases of the nonlocal model for diffusion that the authors consider.
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Image Denoising Methods. A New Nonlocal Principle
TL;DR: A general mathematical and experimental methodology to compare and classify classical image denoising algorithms and a nonlocal means (NL-means) algorithm addressing the preservation of structure in a digital image are defined.
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From Local Kernel to Nonlocal Multiple-Model Image Denoising
TL;DR: This work reviews the evolution of the nonparametric regression modeling in imaging from the local Nadaraya-Watson kernel estimate to the nonlocal means and further to transform-domain filtering based on nonlocal block-matching.
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Tom Goldstein,Stanley Osher +1 more
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A Review of Image Denoising Algorithms, with a New One
TL;DR: A general mathematical and experimental methodology to compare and classify classical image denoising algorithms and a nonlocal means (NL-means) algorithm addressing the preservation of structure in a digital image are defined.