We propose a discretization method for continuous-domain linear inverse problems with
multiple-order total-variation (TV) regularization. It is based on a recent result that proves …

We study one-dimensional continuous-domain inverse problems with multiple generalized
total-variation regularization, which involves the joint use of several regularization operators …

We study continuous-domain linear inverse problems with generalized total-variation (gTV)
regularization, expressed in terms of a regularization operator L. It has recently been proved …

The goal of this thesis is to study continuous-domain inverse problems for the reconstruction
of sparse signals and to develop efficient algorithms to solve such problems …

The formulation of inverse problems in the continuum eliminates discretization errors and
allows for the exact incorporation of priors. In this paper, we formulate a continuous-domain …

We consider one-dimensional (1-D) linear inverse problems that are formulated in the
continuous domain. The object of recovery is a function that is assumed to minimize a …

The total generalized variation (TGV) is a popular regularizer in inverse problems and
imaging combining discontinuous solutions and higher order smoothing. In particular …

Recent advances in machine learning have led to breakthrough developments in many
areas of the applied sciences. In many cases, traditional learning approaches are driven by …

Total variation (TV) is a widely used regularizer for stabilizing the solution of ill-posed
inverse problems. In this paper, we propose a novel proximal-gradient algorithm for …

Splines come in a variety of flavors that can be characterized in terms of some differential
operator L. The simplest piecewise-constant model corresponds to the derivative operator …