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
Iterative methods for total variation denoising
SIAM Journal on Scientific Computing - Special issue on iterative methods in numerical linear algebra; selected papers from the Colorado conference
Exact sampling with coupled Markov chains and applications to statistical mechanics
Proceedings of the seventh international conference on Random structures and algorithms
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIAM Journal on Numerical Analysis
Watersnakes: Energy-Driven Watershed Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
Exact optimization of discrete constrained total variation minimization problems
IWCIA'04 Proceedings of the 10th international conference on Combinatorial Image Analysis
A property of the minimum vectors of a regularizing functionaldefined by means of the absolute norm
IEEE Transactions on Signal Processing
Nonlinear evolution equations as fast and exact solvers of estimation problems
IEEE Transactions on Signal Processing
Exact optimization for Markov random fields with convex priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Restoration with Discrete Constrained Total Variation Part I: Fast and Exact Optimization
Journal of Mathematical Imaging and Vision
A note on the discrete binary Mumford-Shah model
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
Total variation minimization and graph cuts for moving objects segmentation
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Adaptive Variational Method for Restoring Color Images with High Density Impulse Noise
International Journal of Computer Vision
Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction
Journal of Scientific Computing
IEEE Transactions on Image Processing
A Multilevel Algorithm for Simultaneously Denoising and Deblurring Images
SIAM Journal on Scientific Computing
Discrete optimization of the multiphase piecewise constant mumford-shah functional
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
SIAM Journal on Imaging Sciences
How Accurate Can Block Matches Be in Stereo Vision?
SIAM Journal on Imaging Sciences
Total Variation as a Local Filter
SIAM Journal on Imaging Sciences
A vectorial self-dual morphological filter based on total variation minimization
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
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
New algorithms for convex cost tension problem with application to computer vision
Discrete Optimization
Bregmanized Domain Decomposition for Image Restoration
Journal of Scientific Computing
Bregman operator splitting with variable stepsize for total variation image reconstruction
Computational Optimization and Applications
Hi-index | 0.00 |
This paper deals with the minimization of the total variation under a convex data fidelity term. We propose an algorithm which computes an exact minimizer of this problem. The method relies on the decomposition of an image into its level sets. Using these level sets, we map the problem into optimizations of independent binary Markov Random Fields. Binary solutions are found thanks to graph-cut techniques and we show how to derive a fast algorithm. We also study the special case when the fidelity term is the L1-norm. Finally we provide some experiments.