The novel concept of total generalized variation of a function u is introduced, and some of its
essential properties are proved. Differently from the bounded variation seminorm, the new …

Over the last decades, the total variation (TV) has evolved to be one of the most broadly-
used regularisation functionals for inverse problems, in particular for imaging applications …

In this paper we study a variational problem in the space of functions of bounded Hessian.
Our model constitutes a straightforward higher-order extension of the well known ROF …

This paper compares several well known sliding-window methods for denoising crystal
orientation data with variational methods adapted from mathematical image analysis. The …

Total variation methods and similar approaches based on regularizations with ℓ 1-type
norms (and seminorms) have become a very popular tool in image processing and inverse …

Speckle noise contamination is a common issue in ultrasound imaging system. Due to the
edge-preserving feature, total variation (TV) regularization-based techniques have been …

In this work, we investigate image registration in a variational framework and focus on
regularization generality and solver efficiency. We first propose a variational model …

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 …

In this paper, we propose a novel model to restore an image corrupted by blur and Cauchy
noise. The model is composed of a data fidelity term and two regularization terms including …

In this work we analyze and compare two recent variational models for image denoising and
improve their reconstructions by applying a Bregman iteration strategy. One of the standard …