1 citations · 2 across the 9 of their papers we have counts for
4 papers · 1 filter
Learning sparsity-promoting regularizers for linear inverse problems
Giovanni S. Alberti, Ernesto De Vito, Tapio Helin +3
This paper introduces a novel approach to learning sparsity-promoting regularizers for solving linear inverse problems. We develop a bilevel optimization framework to select an opt…
Regularization with optimal space-time priors
Tatiana A. Bubba, Tommi Heikkilä, Demetrio Labate +1
We propose a variational regularization approach based on a multiscale representation called cylindrical shearlets aimed at dynamic imaging problems, especially dynamic tomography.…
Oracle-Net for nonlinear compressed sensing in Electrical Impedance Tomography reconstruction problems
Damiana Lazzaro, Serena Morigi, Luca Ratti
Sparse recovery principles play an important role in solving many nonlinear ill-posed inverse problems. We investigate a variational framework with support Oracle for compressed se…
Learning a Gaussian Mixture for Sparsity Regularization in Inverse Problems
Giovanni S. Alberti, Luca Ratti, Matteo Santacesaria +1
In inverse problems, it is widely recognized that the incorporation of a sparsity prior yields a regularization effect on the solution. This approach is grounded on the a priori as…