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
Convergence of an Iterative Method for Total Variation Denoising
SIAM Journal on Numerical Analysis
A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration
SIAM Journal on Scientific Computing
Oscillating Patterns in Image Processing and Nonlinear Evolution Equations: The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures
Some recent advances in projection-type methods for variational inequalities
Journal of Computational and Applied Mathematics - Proceedings of the international conference on recent advances in computational mathematics
Modeling Textures with Total Variation Minimization and Oscillating Patterns in Image Processing
Journal of Scientific Computing
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
Dual Norms and Image Decomposition Models
International Journal of Computer Vision
Journal of Mathematical Imaging and Vision
On Semismooth Newton's Methods for Total Variation Minimization
Journal of Mathematical Imaging and Vision
Deterministic edge-preserving regularization in computed imaging
IEEE Transactions on Image Processing
Image restoration subject to a total variation constraint
IEEE Transactions on Image Processing
Image decomposition via the combination of sparse representations and a variational approach
IEEE Transactions on Image Processing
The Equivalence of Half-Quadratic Minimization and the Gradient Linearization Iteration
IEEE Transactions on Image Processing
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The main aim of this paper is to accelerate the image decomposition model based on (BV, H 驴1). It is solved with a particularly effective primal-dual gradient descent algorithm. The algorithm works on the primal-dual formulation and exploits the information of the primal and dual variables simultaneously. It converges significantly faster than some popular existing methods in numerical experiments. This approach is to some extent related to projection type methods for solving variational inequalities.