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
Shapes and geometries: analysis, differential calculus, and optimization
Shapes and geometries: analysis, differential calculus, and optimization
High-Order Total Variation-Based Image Restoration
SIAM Journal on Scientific Computing
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
Some First-Order Algorithms for Total Variation Based Image Restoration
Journal of Mathematical Imaging and Vision
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
Variational Methods in Imaging
Variational Methods in Imaging
Denoising time-of-flight data with adaptive total variation
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Homotopy method for a mean curvature-based denoising model
Applied Numerical Mathematics
A Total Variation-Based JPEG Decompression Model
SIAM Journal on Imaging Sciences
Higher-Order TV Methods--Enhancement via Bregman Iteration
Journal of Scientific Computing
Non-convex hybrid total variation for image denoising
Journal of Visual Communication and Image Representation
A Combined First and Second Order Variational Approach for Image Reconstruction
Journal of Mathematical Imaging and Vision
Homogeneous Penalizers and Constraints in Convex Image Restoration
Journal of Mathematical Imaging and Vision
Convex Relaxation of a Class of Vertex Penalizing Functionals
Journal of Mathematical Imaging and Vision
International Journal of Computer Vision
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The novel concept of total generalized variation of a function $u$ is introduced, and some of its essential properties are proved. Differently from the bounded variation seminorm, the new concept involves higher-order derivatives of $u$. Numerical examples illustrate the high quality of this functional as a regularization term for mathematical imaging problems. In particular this functional selectively regularizes on different regularity levels and, as a side effect, does not lead to a staircasing effect.