A fast parametric maximum flow algorithm and applications
SIAM Journal on Computing
Nonlinear total variation based noise removal algorithms
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Iterative methods for total variation denoising
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High-Order Total Variation-Based Image Restoration
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Exact optimization of discrete constrained total variation minimization problems
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Perfect sampling: a review and applications to signal processing
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Exact optimization for Markov random fields with convex priors
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A comparison of three total variation based texture extraction models
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IEEE Transactions on Signal Processing
Efficient minimization method for a generalized total variation functional
IEEE Transactions on Image Processing
A fast optimization transfer algorithm for image inpainting in wavelet domains
IEEE Transactions on Image Processing
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Weighted and extended total variation for image restoration and decomposition
Pattern Recognition
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Computational Statistics & Data Analysis
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Computational Optimization and Applications
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SIAM Journal on Imaging Sciences
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SIAM Journal on Imaging Sciences
Domain decomposition methods with graph cuts algorithms for total variation minimization
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Journal of Electrical and Computer Engineering
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Journal of Mathematical Imaging and Vision
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This paper deals with the total variation minimization problem in image restoration for convex data fidelity functionals. We propose a new and fast algorithm which computes an exact solution in the discrete framework. Our method relies on the decomposition of an image into its level sets. It maps the original problems into independent binary Markov Random Field optimization problems at each level. Exact solutions of these binary problems are found thanks to minimum cost cut techniques in graphs. These binary solutions are proved to be monotone increasing with levels and yield thus an exact solution of the discrete original problem. Furthermore we show that minimization of total variation under L 1 data fidelity term yields a self-dual contrast invariant filter. Finally we present some results.