4 citations · 5 across the 6 of their papers we have counts for
9 papers
Automatic parameter selection for the TGV regularizer in image restoration under Poisson noise
Daniela di Serafino, Monica Pragliola
We address the image restoration problem under Poisson noise corruption. The Kullback-Leibler divergence, which is typically adopted in the variational framework as data fidelity t…
Numerical Solution of Stiff ODEs with Physics-Informed RPNNs
Evangelos Galaris, Gianluca Fabiani, Francesco Calabrò +2
We propose a numerical method based on physics-informed Random Projection Neural Networks for the solution of Initial Value Problems (IVPs) of Ordinary Differential Equations (ODEs…
TGV-based restoration of Poissonian images with automatic estimation of the regularization parameter
Daniela di Serafino, Germana Landi, Marco Viola
The problem of restoring images corrupted by Poisson noise is common in many application fields and, because of its intrinsic ill posedness, it requires regularization techniques f…
Sparse Approximations with Interior Point Methods
Valentina De Simone, Daniela di Serafino, Jacek Gondzio +2
Large-scale optimization problems that seek sparse solutions have become ubiquitous. They are routinely solved with various specialized first-order methods. Although such methods a…
Spatially Adaptive Regularization in Image Segmentation
Laura Antonelli, Valentina De Simone, Daniela di Serafino
We modify the total-variation-regularized image segmentation model proposed by Chan, Esedoglu and Nikolova [SIAM Journal on Applied Mathematics 66, 2006] by introducing local regul…
Using gradient directions to get global convergence of Newton-type methods
Daniela di Serafino, Gerardo Toraldo, Marco Viola
The renewed interest in Steepest Descent (SD) methods following the work of Barzilai and Borwein [IMA Journal of Numerical Analysis, 8 (1988)] has driven us to consider a globaliza…
A subspace-accelerated split Bregman method for sparse data recovery with joint l1-type regularizers
Valentina De Simone, Daniela di Serafino, Marco Viola
We propose a subspace-accelerated Bregman method for the linearly constrained minimization of functions of the form ,…
Constraint-Preconditioned Krylov Solvers for Regularized Saddle-Point Systems
Daniela di Serafino, Dominique Orban
We consider the iterative solution of regularized saddle-point systems. When the leading block is symmetric and positive semi-definite on an appropriate subspace, Dollar, Gould, Sc…
ACQUIRE: an inexact iteratively reweighted norm approach for TV-based Poisson image restoration
Daniela di Serafino, Germana Landi, Marco Viola
We propose a method, called ACQUIRE, for the solution of constrained optimization problems modeling the restoration of images corrupted by Poisson noise. The objective function is…