tvreg: Variational Imaging Methods. The tvreg package performs total variation (TV) regularized image denoising, deconvolution, and inpainting. Three different noise models are supported: Gaussian (L2), Laplace (L1), and Poisson. The implementation solves the general TV restoration problem: min_u TV(u) + int lambda F(K*u,f) dx .to perform denoising, deconvolution, and inpainting as special cases. It is efficiently solved using the recent split Bregman method. Also included is an efficient implementation of Chan-Vese two-phase segmentation. All functions support grayscale, color, and arbitrary multichannel images.
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References in zbMATH (referenced in 10 articles )
Showing results 1 to 10 of 10.
- Maiseli, Baraka Jacob; Gao, Huijun: Robust edge detector based on anisotropic diffusion-driven process (2016)
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- Maiseli, Baraka; Wu, Chuan; Mei, Jiangyuan; Liu, Qiang; Gao, Huijun: A robust super-resolution method with improved high-frequency components estimation and aliasing correction capabilities (2014)
- Petro, Ana Belén; Sbert, Catalina; Morel, Jean-Michel: Automatic correction of image intensity non-uniformity by the simplest total variation model (2014)
- Storath, Martin; Weinmann, Andreas: Fast partitioning of vector-valued images (2014)
- Weinmann, Andreas; Demaret, Laurent; Storath, Martin: Total variation regularization for manifold-valued data (2014)
- Lanza, Alessandro; Morigi, Serena; Sgallari, Fiorella; Yezzi, Anthony J.: Variational image denoising based on autocorrelation whiteness (2013)
- Lebrun, M.; Buades, A.; Morel, J.M.: A nonlocal Bayesian image denoising algorithm (2013)
- Louchet, Cécile; Moisan, Lionel: Posterior expectation of the total variation model: properties and experiments (2013)