tvreg

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.


References in zbMATH (referenced in 21 articles )

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  1. Holler, Martin; Weinmann, Andreas: Non-smooth variational regularization for processing manifold-valued data (2020)
  2. Kumar, Sumit; Jha, Rajib Kumar: An FPGA-based design for a real-time image denoising using approximated fractional integrator (2020)
  3. Mead, J.: ( \chi^2) test for total variation regularization parameter selection (2020)
  4. Ben Said, Ahmed; Hadjidj, Rachid; Foufou, Sebti: Total variation for image denoising based on a novel smart edge detector: an application to medical images (2019)
  5. Wang, Wei; Xia, Xiang-Gen; Zhang, Shengli; He, Chuanjiang; Chen, Ling: Vector total fractional-order variation and its applications for color image denoising and decomposition (2019)
  6. You, Juntao; Jiao, Yuling; Lu, Xiliang; Zeng, Tieyong: A nonconvex model with minimax concave penalty for image restoration (2019)
  7. Campagna, Rosanna; Crisci, Serena; Cuomo, Salvatore; Marcellino, Livia; Toraldo, Gerardo: Modification of TV-ROF denoising model based on split Bregman iterations (2017)
  8. Borkowski, Dariusz: Forward and backward filtering based on backward stochastic differential equations (2016)
  9. Lu, Wenqi; Duan, Jinming; Qiu, Zhaowen; Pan, Zhenkuan; Liu, Ryan Wen; Bai, Li: Implementation of high-order variational models made easy for image processing (2016)
  10. Maiseli, Baraka Jacob; Gao, Huijun: Robust edge detector based on anisotropic diffusion-driven process (2016)
  11. Orović, Irena; Lekić, Nedjeljko; Stanković, Srdjan: An analog-digital hardware for L-estimate space-varying image filtering (2016)
  12. Coll, Bartomeu; Duran, Joan; Sbert, Catalina: Half-linear regularization for nonconvex image restoration models (2015)
  13. Batard, Thomas; Bertalmío, Marcelo: On covariant derivatives and their applications to image regularization (2014)
  14. Burger, M.; Müller, J.; Papoutsellis, E.; Schönlieb, C. B.: Total variation regularization in measurement and image space for PET reconstruction (2014)
  15. 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)
  16. Petro, Ana Belén; Sbert, Catalina; Morel, Jean-Michel: Automatic correction of image intensity non-uniformity by the simplest total variation model (2014)
  17. Storath, Martin; Weinmann, Andreas: Fast partitioning of vector-valued images (2014)
  18. Weinmann, Andreas; Demaret, Laurent; Storath, Martin: Total variation regularization for manifold-valued data (2014)
  19. Lanza, Alessandro; Morigi, Serena; Sgallari, Fiorella; Yezzi, Anthony J.: Variational image denoising based on autocorrelation whiteness (2013)
  20. Lebrun, M.; Buades, A.; Morel, J. M.: A nonlocal Bayesian image denoising algorithm (2013)

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