The restoration of blurred images corrupted by Poisson noise is an important task in various
applications such as astronomical imaging, electronic microscopy, single particle emission …

In this paper, we consider a variational restoration model consisting of the I-divergence as
data fitting term and the total variation semi-norm or nonlocal means as regularizer for …

We examine relations between popular variational methods in image processing and
classical operator splitting methods in convex analysis. We focus on a gradient descent …

We examine the underlying structure of popular algorithms for variational methods used in
image processing. We focus here on operator splittings and Bregman methods based on a …

We present in this paper the motivation and theory of nonlinear spectral representations,
based on convex regularizing functionals. Some comparisons and analogies are drawn to …

There are two popular ways to implement anisotropic diffusion filters with a diffusion tensor:
Explicit finite difference schemes are simple but become inefficient due to severe time step …

A new total variation (TV) spectral framework is presented. A TV transform is proposed which
can be interpreted as a spectral domain, where elementary TV features, such as disks …

This paper discusses the use of absolutely one-homogeneous regularization functionals in a
variational, scale space, and inverse scale space setting to define a nonlinear spectral …

The main topic of this study is the microstructure of catalyst layers (CLs) of a high-
temperature gas diffusion electrode in a PEM fuel cell, bonded by polybenzimidazole. Using …

We investigate a class of efficient numerical algorithms for many partial differential equations
(PDEs) in image analysis. They are applicable to parabolic or elliptic PDEs that have …