93 citations · 263 across the 8 of their papers we have counts for
8 papers
A Line--Search--Based Stochastic Gradient Method for 3D Computed Tomography
Tatiana A. Bubba, Elena Morotti, Federica Porta +2
We introduce FB-LISA, a forward-backward (FB) generalization of a recently proposed line-search-based stochastic gradient algorithm to address the imaging problem of volumetric rec…
Block-coordinate Plug-And-Play Methods with Armijo-like line-search for Image Restoration
Federica Porta, Simone Rebegoldi, Andrea Sebastiani
In this paper, we develop a class of block-coordinate forward-backward methods for solving non-convex and non-separable composite optimization problems, and specialize them to imag…
Combining Weighted Total Variation and Deep Image Prior for natural and medical image restoration via ADMM
Pasquale Cascarano, Andrea Sebastiani, Maria Colomba Comes +2
In the last decades, unsupervised deep learning based methods have caught researchers attention, since in many real applications, such as medical imaging, collecting a great amount…
On the convergence of a linesearch based proximal-gradient method for nonconvex optimization
Silvia Bonettini, Ignace Loris, Federica Porta +2
We consider a variable metric linesearch based proximal gradient method for the minimization of the sum of a smooth, possibly nonconvex function plus a convex, possibly nonsmooth t…
A variable metric forward--backward method with extrapolation
Silvia Bonettini, Federica Porta, Valeria Ruggiero
Forward-backward methods are a very useful tool for the minimization of a functional given by the sum of a differentiable term and a nondifferentiable one and their investigation h…
Variable metric inexact line-search based methods for nonsmooth optimization
Silvia Bonettini, Ignace Loris, Federica Porta +1
We develop a new proximal-gradient method for minimizing the sum of a differentiable, possibly nonconvex, function plus a convex, possibly non differentiable, function. The key fea…
A new steplength selection for scaled gradient methods with application to image deblurring
Federica Porta, Marco Prato, Luca Zanni
Gradient methods are frequently used in large scale image deblurring problems since they avoid the onerous computation of the Hessian matrix of the objective function. Second order…
On the filtering effect of iterative regularization algorithms for linear least-squares problems
Anastasia Cornelio, Federica Porta, Marco Prato +1
Many real-world applications are addressed through a linear least-squares problem formulation, whose solution is calculated by means of an iterative approach. A huge amount of stud…