A note on the gradient of a multi-image
Computer Vision, Graphics, and Image Processing - Lectures notes in computer science, Vol. 201 (G. Goos and J. Hartmanis, Eds.)
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration
SIAM Journal on Scientific Computing
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
A Variational Approach to Remove Outliers and Impulse Noise
Journal of Mathematical Imaging and Vision
Dual Norms and Image Decomposition Models
International Journal of Computer Vision
Structure-Texture Image Decomposition--Modeling, Algorithms, and Parameter Selection
International Journal of Computer Vision
Image Restoration with Discrete Constrained Total Variation Part I: Fast and Exact Optimization
Journal of Mathematical Imaging and Vision
Fast Global Minimization of the Active Contour/Snake Model
Journal of Mathematical Imaging and Vision
Projected Gradient Based Color Image Decomposition
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Locally Adaptive Total Variation Regularization
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
The Split Bregman Method for L1-Regularized Problems
SIAM Journal on Imaging Sciences
Journal of Mathematical Imaging and Vision
An Augmented Lagrangian Method for TVg+L1-norm Minimization
Journal of Mathematical Imaging and Vision
A Multi-Scale Vectorial Lτ-TV Framework for Color Image Restoration
International Journal of Computer Vision
Global Solutions of Variational Models with Convex Regularization
SIAM Journal on Imaging Sciences
Total variation minimization and a class of binary MRF models
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Color TV: total variation methods for restoration of vector-valued images
IEEE Transactions on Image Processing
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In this paper we propose to investigate the use of a vectorial total variation model with spatially varying regularization and data terms for color image denoising and restoration. We pay attention to two main minimization problems: the minimization of a weighted vectorial total variation term TVg, which acts as a regularization term, using the L2 norm as data term or the minimization of the vectorial total variation with a spatially varying $L^1_g$ norm. The optimization process takes benefit of convex optimization tools by introducing an augmented Lagrangian formulation. This formulation leads us to simple and efficient algorithms based on Uzawa block relaxation schemes that are also robust towards the choice of the penalty parameter. In this paper, We propose to study more particularly the impact of spatially varying terms (total variation term or data terms) for color image restoration. A new weighted total variation term is proposed for old parchments restoration and we also compare the use of a weighted total variation term with a spatially varying data term for impulse noise removal in color images.