This paper gives a unified convergence analysis of additive Schwarz methods for general
convex optimization problems. Resembling the fact that additive Schwarz methods for linear …

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 …

Nowadays, as large scale images become available, the necessity of parallel algorithms for
image processing has been arisen. In this paper, we propose domain decomposition …

We consider a finite element discretization for the dual Rudin--Osher--Fatemi model using a
Raviart--Thomas basis for H_0(div;Ω). Since the proposed discretization has a splitting …

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 …

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 …

In ptychography experiments, redundant scanning is usually required to guarantee the
stable recovery, such that a huge number of frames are generated, and thus it poses a great …

We concern with fast domain decomposition methods for solving the total variation
minimization problems in image processing. By decomposing the image domain into non …

Total variation denoising (TVD) is an effective technique for image denoising, in particular,
for recovering blocky, discontinuous images from noisy background. The problem is …