Journal of Computational Physics
Numerical methods for minimization problems constrained to S1 and S2
Journal of Computational Physics
Channel Smoothing: Efficient Robust Smoothing of Low-Level Signal Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convex Hodge Decomposition and Regularization of Image Flows
Journal of Mathematical Imaging and Vision
An Improved LOT Model for Image Restoration
Journal of Mathematical Imaging and Vision
A Nonlinear Structure Tensor with the Diffusivity Matrix Composed of the Image Gradient
Journal of Mathematical Imaging and Vision
Anisotropic Smoothing Using Double Orientations
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Image Denoising Using TV-Stokes Equation with an Orientation-Matching Minimization
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
New Riemannian techniques for directional and tensorial image data
Pattern Recognition
A TV-stokes denoising algorithm
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Multigrid Algorithm for High Order Denoising
SIAM Journal on Imaging Sciences
Orientation-Matching Minimization for Image Denoising and Inpainting
International Journal of Computer Vision
A parameter study of a hybrid Laplacian mean-curvature flow denoising model
The Journal of Supercomputing
A fast implementation algorithm of TV inpainting model based on operator splitting method
Computers and Electrical Engineering
On vector and matrix median computation
Journal of Computational and Applied Mathematics
Mumford-Shah-Euler Flow with Sphere Constraint and Applications to Color Image Inpainting
SIAM Journal on Imaging Sciences
Lagrangian multipliers and split Bregman methods for minimization problems constrained on Sn-1
Journal of Visual Communication and Image Representation
A Splitting Method for Orthogonality Constrained Problems
Journal of Scientific Computing
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We propose in this paper an alternative approach for computing p-harmonic maps and flows: instead of solving a constrained minimization problem on SN-1, we solve an unconstrained minimization problem on the entire space of functions. This is possible, using the projection on the sphere of any arbitrary function. Then we show how this formulation can be used in practice, for problems with both isotropic and anisotropic diffusion, with applications to image processing, using a new finite difference scheme.