51 citations · 388 across the 44 of their papers we have counts for
6 papers · 1 filter
Infinite dimensional generative sensing
Paolo Angella, Vito Paolo Pastore, Matteo Santacesaria
Deep generative models have become a standard for modeling priors for inverse problems, going beyond classical sparsity-based methods. However, existing theoretical guarantees are…
Deep Unfolding Network for Nonlinear Multi-Frequency Electrical Impedance Tomography
Giovanni S. Alberti, Damiana Lazzaro, Serena Morigi +2
Multi-frequency Electrical Impedance Tomography (mfEIT) represents a promising biomedical imaging modality that enables the estimation of tissue conductivities across a range of fr…
Localization of point scatterers via sparse optimization on measures
Giovanni S. Alberti, Romain Petit, Matteo Santacesaria
We consider the inverse scattering problem for time-harmonic acoustic waves in a medium with pointwise inhomogeneities. In the Foldy-Lax model, the estimation of the scatterers' lo…
Neural networks for classification of strokes in electrical impedance tomography on a 3D head model
Valentina Candiani, Matteo Santacesaria
We consider the problem of the detection of brain hemorrhages from three dimensional (3D) electrical impedance tomography (EIT) measurements. This is a condition requiring urgent t…
Direct inversion from partial-boundary data in electrical impedance tomography
Andreas Hauptmann, Matteo Santacesaria, Samuli Siltanen
In Electrical Impedance Tomography (EIT) one wants to image the conductivity distribution of a body from current and voltage measurements carried out on its boundary. In this paper…
Positive-energy D-bar method for acoustic tomography: a computational study
Maarten de Hoop, Matti Lassas, Matteo Santacesaria +2
A new computational method for reconstructing a potential from the Dirichlet-to-Neumann map at positive energy is developed. The method is based on D-bar techniques and it works in…