The minimization of a functional composed of a nonsmooth and nonadditive regularization
term and a combined L^1 and L^2 data-fidelity term is proposed. It is shown analytically and …

Algorithms for automatically selecting a scalar or locally varying regularization parameter for
total variation models with an L^ τ L τ-data fidelity term, τ ∈ {1, 2\} τ∈ 1, 2, are presented …

This paper is concerned with overlapping domain decomposition methods (DDMs), based
on successive subspace correction (SSC) and parallel subspace correction (PSC), for the …

Total variation minimization is standard in mathematical imaging and there have been
numerous researches over the last decades. In order to process large-scale images in real …

In this paper non-overlapping domain decomposition methods for the pre-dual total variation
minimization problem are introduced. Both parallel and sequential approaches are …

Nonlocal total variation (TV) regularization (Gilboa and Osher in Multiscale Model Simulat 7
(3): 1005–1028, 2008; Zhou and Schölkopf in Pattern recognition, proceedings of the 27th …

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 …

We consider nonoverlapping domain decomposition methods for the Rudin--Osher--Fatemi
(ROF) model, which is one of the standard models in mathematical image processing. The …

Subspace techniques such as Krylov subspace methods have been well known and
extensively used in numerical linear algebra. They are also ubiquitous and becoming …

This paper presents the domain decomposition methods (DDMs) for achieving fast parallel
computing on multi-core computers when dealing with the multi-phase labeling problem. To …