The successful application of univariate morphological operators on several domains, along
with the increasing need for processing the plethora of available multivalued images, have …

We introduce a new nonsmooth variational model for the restoration of manifold-valued data
which includes second order differences in the regularization term. While such models were …

We present a PDE-based framework that generalizes Group equivariant Convolutional
Neural Networks (G-CNNs). In this framework, a network layer is seen as a set of PDE …

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 …

We develop a concept for the median filtering of tensor data. The main part of this concept is
the definition of median for symmetric matrices. This definition is based on the minimisation …

We extend the well-known scalar image bilateral filtering technique to diffusion tensor
magnetic resonance images (DTMRI). The scalar version of bilateral image filtering is …

Matrix-valued data sets arise in a number of applications including diffusion tensor magnetic
resonance imaging (DT-MRI) and physical measurements of anisotropic behaviour …

Matrix fields are becoming increasingly important in digital imaging. In order to perform
shape analysis, enhancement or segmentation of such matrix fields, appropriate image …

A variant of morphological attribute filters is developed, in which the attribute on which
filtering is based, is no longer a scalar, as is usual, but a vector. This leads to new …

The growing interest in the use of clean energy has led to the construction of increasingly
large photovoltaic systems. Consequently, monitoring the proper functioning of these …