Nonlinear functional analysis and its applications
Nonlinear functional analysis and its applications
Feature-oriented image enhancement using shock filters
SIAM Journal on Numerical Analysis
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
Oscillating Patterns in Image Processing and Nonlinear Evolution Equations: The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
Modeling Textures with Total Variation Minimization and Oscillating Patterns in Image Processing
Journal of Scientific Computing
Tube Methods for BV Regularization
Journal of Mathematical Imaging and Vision
Proceedings of the 4th international conference on Scale space methods in computer vision
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Image decomposition application to SAR images
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
The Equivalence of the Taut String Algorithm and BV-Regularization
Journal of Mathematical Imaging and Vision
Bivariate density estimation using BV regularisation
Computational Statistics & Data Analysis
Relations between higher order TV regularization and support vector regression
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
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In this paper we derive a unified framework for the taut-string algorithm and regularization with G-norm data fit. The G-norm data fit criterion (popularized in image processing by Y. Meyer) has been paid considerable interest in regularization models for pattern recognition. The first numerical work based on G-norm data fit has been proposed by Osher and Vese. The taut-string algorithm has been developed in statistics (Mammen and van de Geer and Davies and Kovac) for denoising of one dimensional sample data of a discontinuous function. Recently Hinterberger et al. proposed an extension of the taut-string algorithm to higher dimensional data by introducing the concept of tube methods. Here we highlight common features between regularization programs with a G-norm data fit term and taut-string algorithms (respectively tube methods). This links the areas of statistics, regularization theory, and image processing.