A very fast noniterative algorithm is proposed for denoising or smoothing one-dimensional
discrete signals, by solving the total variation regularized least-squares problem or the …

This paper seeks to combine linear time-invariant (LTI) filtering and sparsity-based
denoising in a principled way in order to effectively filter (denoise) a wider class of signals …

We study TV regularization, a widely used technique for eliciting structured sparsity. In
particular, we propose efficient algorithms for computing prox-operators for lp-norm TV. The …

This article studies the denoising performance of total variation (TV) image regularization.
More precisely, we study geometrical properties of the solution to the so-called Rudin-Osher …

The total variation image (or signal) denoising model is a variational approach that can be
interpreted, in a Bayesian framework, as a search for the maximum point of the posterior …

Abstract The Mumford–Shah model is a very powerful variational approach for edge
preserving regularization of image reconstruction processes. However, it is algorithmically …

We consider the problem of minimizing the continuous valued total variation subject to
different unary terms on trees and propose fast direct algorithms based on dynamic …

In this paper we study the structure of solutions of the one dimensional weighted total
variation regularisation problem, motivated by its application in signal recovery tasks. We …

In this paper we are concerned with fully automatic and locally adaptive estimation of
functions in a “signal+ noise”-model where the regression function may additionally be …

Snow avalanche victims have only a good chance to survive when they are located within a
short time. This requires an active beacon for them to wear or a very rapid deployment of a …