Algorithms based on total variation (TV) minimization are prevalent in image processing.
They play a key role in a variety of applications such as image denoising, compressive …

We introduce a class of adaptive non-smooth convex variational problems for image
denoising in terms of a common data fitting term and a support functional as regularizer …

For denoising depth maps from time-of-flight (ToF) cameras we propose an adaptive total
variation based approach of first and second order. This approach allows us to take into …

We consider solution-driven adaptive variants of Total Variation, in which the adaptivity is
introduced as a fixed point problem. We provide existence theory for such fixed points in the …

We consider a class of quasi-variational inequalities (QVIs) for adaptive image restoration,
where the adaptivity is described via solution-dependent constraint sets. In previous work …

We introduce a new general TV regularizer, namely, generalized TV regularization, to study
image denoising and nonblind image deblurring problems. In order to discuss the …

Image restoration and enhancement methods that respect important features such as edges
play a fundamental role in digital image processing. In the last decades a large variety of …

We consider a class of quasi-variational inequalities (QVIs) for adaptive image restoration,
where the adaptivity is described via solution-dependent constraint sets. In previous work …