We introduce a new nonsmooth variational model for the restoration of manifold-valued data which includes second order differences in the regularization term, which up to now only existed for cyclic data.Expand

In many image and signal processing applications, such as interferometric synthetic aperture radar (SAR), electroencephalogram (EEG) data analysis, ground-based astronomy, and color image restoration, in HSV or LCh spaces the data has its range on the one-dimensional sphere $\mathbb S^1$.Expand

The paper addresses the generalization of the half-quadratic minimization method for the restoration of images having values in a complete Riemannian manifold. We recall the half-quadratic… Expand

We develop algorithms for the solution of the corresponding second-order total variation-type problems for denoising, inpainting as well as the combination of both.Expand

We are interested in restoring images having values in a symmetric Hadamard manifold by minimizing a functional with a quadratic data term and a total variation--like regularizing term. To solve the convex minimization problem, we extend the Douglas--Rachford algorithm and its parallel version to symmetric manifolds.Expand

We generalize discrete variational models involving the infimal convolution (IC) of first and second order differences and the total generalized variation (TGV) to manifold-valued images.Expand

Graph-based methods have been proposed as a unified framework for discrete calculus of local and nonlocal image processing methods in recent years.Expand

We introduce a new graph infinity-Laplace operator based on the idea of discrete minimizing Lipschitz extensions, which we use to formulate the inpainting problem as PDE on the graph.Expand

We propose a new iterative multiplicative filtering algorithm for label assignment matrices which can be used for the supervised partitioning of manifold-valued images.Expand