Numerical methods for minimization problems constrained to \(S^1\) and \(S^2\)
DOI10.1016/j.jcp.2004.01.020zbMath1051.65072OpenAlexW2092478502MaRDI QIDQ1879614
Luminita A. Vese, Stanley J. Osher, Thomas C. Cecil
Publication date: 23 September 2004
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jcp.2004.01.020
minimization problemsnumerical examplesharmonic mapsgradient descent methodcoloring imagesdenoising of directional data
Numerical optimization and variational techniques (65K10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Existence theories for optimal control problems involving partial differential equations (49J20) Discrete approximations in optimal control (49M25)
Related Items (2)
Cites Work
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- Nonlinear total variation based noise removal algorithms
- Regularity of minimizing harmonic maps into the sphere
- Some results on uniformly high-order accurate essentially nonoscillatory schemes
- Asymptotics for the minimization of a Ginzburg-Landau functional
- Variational problems for maps of bounded variation with values in \(S^ 1\)
- Solving variational problems and partial differential equations mapping into general target manifolds.
- Orthonormal vector sets regularization with PDE's and applications
- Variational Restoration of Nonflat Image Features: Models and Algorithms
- Numerical Methods for the Landau--Lifshitz Equation
- A New Algorithm For Computing Liquid Crystal Stable Configurations: The Harmonic Mapping Case
- Color image enhancement via chromaticity diffusion
- A general framework for low level vision
- Numerical Methods forp-Harmonic Flows and Applications to Image Processing
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