Locally Adaptive Total Variation Regularization

Authors:
Markus Grasmair
Affiliations:
Department of Mathematics, University of Innsbruck, Innsbruck, Austria A-6020
Venue:
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Year:
2009

Citing 7
Cited 4

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
Convex analysis and variational problems

Convex analysis and variational problems
Augmented Lagrangian methods for nonsmooth, convex optimization in Hilbert spaces

Nonlinear Analysis: Theory, Methods & Applications
An Algorithm for Total Variation Minimization and Applications

Journal of Mathematical Imaging and Vision
Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)

Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)
Variational Methods in Imaging

Variational Methods in Imaging
Variational denoising of partly textured images by spatially varying constraints

IEEE Transactions on Image Processing

An Augmented Lagrangian Method for TVg+L1-norm Minimization

Journal of Mathematical Imaging and Vision
Variational image denoising with adaptive constraint sets

SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Uzawa block relaxation methods for color image restoration

ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Statistical Multiresolution Estimation for Variational Imaging: With an Application in Poisson-Biophotonics

Journal of Mathematical Imaging and Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

We introduce a locally adaptive parameter selection method for total variation regularization applied to image denoising. The algorithm iteratively updates the regularization parameter depending on the local smoothness of the outcome of the previous smoothing step. In addition, we propose an anisotropic total variation regularization step for edge enhancement. Test examples demonstrate the capability of our method to deal with varying, unknown noise levels.