Political theory of European union, through an engagement between political concepts and theoretical understandings, provides a means of identifying the EU as a political object. It is argued that understanding the projects, processes and products of European union, based on ‘sharing’ or ‘communion’, provides a better means of perceiving the EU as a political object rather than terms such as ‘inte

Drought and heat events, such as the 2018 European drought, interact with the exchange of energy between the land surface and the atmosphere, potentially affecting albedo, sensible and latent heat fluxes, as well as CO 2 exchange. Each of these quantities may aggravate or mitigate the drought, heat, their side effects on productivity, water scarcity and global warming. We used measurements of 56 e

The nature of the Neolithic Pitted Ware Culture (PWC) has been debated in Scandinavian archaeology since the beginning of the 20th century. This material culture post-dates the inception of an agro-pastoral Neolithic economy in the region (TRB) but demonstrate a semi-foraging lifeway. The PWC is considered elusive in the sense that the economy has been interpreted as either based on maritime forag

Alternating and parallel overlapping domain decomposition methods for the minimization of the total variation are presented. Their derivation is based on the predual formulation of the total variation minimization problem. In particular, the predual total variation minimization problem is decomposed into overlapping domains yielding subdomain problems in the respective dual space. Subsequently the

The minimization of a functional consisting of a combined L1/L2 data fidelity term and a total variation regularization term with a locally varying regularization parameter for the removal of mixed Gaussian–impulse noise is considered. Based on a related locally constrained optimization problem, algorithms for automatically selecting the spatially varying parameter are presented. Numerical experim

In this paper, we investigate the usefulness of adding a box-constraint to the minimization of functionals consisting of a data-fidelity term and a total variation regularization term. In particular, we show that in certain applications an additional box-constraint does not effect the solution at all, i.e., the solution is the same whether a box-constraint is used or not. On the contrary, i.e., f

Choices of regularization parameters are central to variational methods for image restoration. In this paper, a spatially adaptive (or distributed) regularization scheme is developed based on localized residuals, which properly balances the regularization weight between regions containing image details and homogeneous regions. Surrogate iterative methods are employed to handle given subsampled dat

The minimization of a functional consisting of a combined L 1/L 2-data-fidelity term and a total variation term, named L 1-L 2-TV model, is considered to remove a mixture of Gaussian and impulse noise in images, which are possibly additionally deformed by some convolution operator. We investigate analytically the stability of this model with respect to its parameters and link it to a constrained m

Based on the weighted total variation model and its analysis pursued in Hintermüller and Rautenberg 2016, in this paper a continuous, i.e., infinite dimensional, projected gradient algorithm and its convergence analysis are presented. The method computes a stationary point of a regularized bilevel optimization problem for simultaneously recovering the image as well as determining a spatially distr

Algorithms for automatically selecting a scalar or locally varying regularization parameter for total variation models with an Lτ-data fidelity term, τ∈ { 1 , 2 } , are presented. The automated selection of the regularization parameter is based on the discrepancy principle, whereby in each iteration a total variation model has to be minimized. In the case of a locally varying parameter, this amoun

In this paper non-overlapping domain decomposition methods for the pre-dual total variation minimization problem are introduced. Both parallel and sequential approaches are proposed for these methods for which convergence to a minimizer of the original problem is established. The associated subproblems are solved by a semi-smooth Newton method. Several numerical experiments are presented, which sh

The minimization of a functional composed of a nonsmooth and nonadditive regularization term and a combined L1 and L2 data-fidelity term is proposed. It is shown analytically and numerically that the new model has noticeable advantages over popular models in image processing tasks. For the numerical minimization of the new objective, subspace correction methods are introduced which guarantee the c