5 papers
Directional TGV-based image restoration under Poisson noise
Daniela di Serafino, Germana Landi, Marco Viola
We are interested in the restoration of noisy and blurry images where the texture mainly follows a single direction (i.e., directional images). Problems of this type arise, for exa…
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…
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 ,…
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…