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
Convex analysis and variational problems
Convex analysis and variational problems
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
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Global Minimization of the Active Contour/Snake Model
Journal of Mathematical Imaging and Vision
A Convex Formulation of Continuous Multi-label Problems
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
A New Total Variation Method for Multiplicative Noise Removal
SIAM Journal on Imaging Sciences
A Nonlinear Inverse Scale Space Method for a Convex Multiplicative Noise Model
SIAM Journal on Imaging Sciences
Removing Multiplicative Noise by Douglas-Rachford Splitting Methods
Journal of Mathematical Imaging and Vision
Iterative weighted maximum likelihood denoising with probabilistic patch-based weights
IEEE Transactions on Image Processing
Multiplicative Noise Removal Using L1 Fidelity on Frame Coefficients
Journal of Mathematical Imaging and Vision
Deblurring Poissonian images by split Bregman techniques
Journal of Visual Communication and Image Representation
Multiplicative noise removal using variable splitting and constrained optimization
IEEE Transactions on Image Processing
Multiplicative Noise Removal with Spatially Varying Regularization Parameters
SIAM Journal on Imaging Sciences
SIAM Journal on Imaging Sciences
Global Minimization for Continuous Multiphase Partitioning Problems Using a Dual Approach
International Journal of Computer Vision
Exact optimization for Markov random fields with convex priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
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We propose a convex relaxation technique for computing global solutions for the nonconvex multiplicative noise model. The method is based on functional lifting by introducing an additional dimension. We employ a primal-dual-based gradient-type algorithm in numerical implementations to overcome the nondifferentiability of the total variation term. Numerical results show that our algorithm is highly efficient. Furthermore, global solutions of the original model can be obtained with no dependence on the initial guess.