Variational image denoising with adaptive constraint sets

Authors:
Frank Lenzen;Florian Becker;Jan Lellmann;Stefania Petra;Christoph Schnörr
Affiliations:
HCI & IPA, Heidelberg University, Heidelberg, Germany;HCI & IPA, Heidelberg University, Heidelberg, Germany;HCI & IPA, Heidelberg University, Heidelberg, Germany;HCI & IPA, Heidelberg University, Heidelberg, Germany;HCI & IPA, Heidelberg University, Heidelberg, Germany
Venue:
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Year:
2011

Citing 6
Cited 2

The algebraic eigenvalue problem

The algebraic eigenvalue problem
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
An Algorithm for Total Variation Minimization and Applications

Journal of Mathematical Imaging and Vision
Multi-scale Total Variation with Automated Regularization Parameter Selection for Color Image Restoration

SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Locally Adaptive Total Variation Regularization

SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Anisotropic Smoothing Using Double Orientations

SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision

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
A class of quasi-variational inequalities for adaptive image denoising and decomposition

Computational Optimization and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a generalization of the total variation (TV) minimization method proposed by Rudin, Osher and Fatemi. This generalization allows for adaptive regularization, which depends on the minimizer itself. Existence theory is provided in the framework of quasi-variational inequalities. We demonstrate the usability of our approach by considering applications for image and movie denoising.