1 citations · 1 across the 5 of their papers we have counts for
5 papers
Iterated graph Laplacian for image restoration problems
Stefano Aleotti, Davide Bianchi, Florian Bossmann +2
We study the graph Laplacian operator as a regularizer in a generalized Tikhonov framework for linear ill-posed problems. The Laplacian is updated iteratively from the current reco…
Multilevel Preconditioning Strategies for Convex Optimization Methods in Image Deblurring
Stefano Aleotti, Claudia Binda, Marco Donatelli +1
Proximal gradient methods are widely used in imaging, and their speed of convergence can be accelerated by incorporating variable metrics and/or extrapolation steps. Recent works h…
Variable Projected Augmented Lagrangian Methods for Generalized Lasso Problems
Stefano Aleotti, Davide Bianchi, Florian Bossmann +2
We introduce variable projected augmented Lagrangian (VPAL) methods for solving generalized nonlinear Lasso problems with improved speed and accuracy. By eliminating the nonsmooth…
A Preconditioned Version of a Nested Primal-Dual Algorithm for Image Deblurring
Stefano Aleotti, Marco Donatelli, Rolf Krause +1
Variational models for image deblurring problems typically consist of a smooth term and a potentially non-smooth convex term. A common approach to solving these problems is using p…
A data-dependent regularization method based on the graph Laplacian
Davide Bianchi, Davide Evangelista, Stefano Aleotti +3
We investigate a variational method for ill-posed problems, named , which embeds a graph Laplacian operator in the regularization term. The novelty of this meth…