1 citations · 2 across the 9 of their papers we have counts for
9 papers
A geometry-based deep equilibrium model for image restoration under multiplicative Gamma noise
Shengkun Yang, Luca Ratti, Zhichang Guo
We propose a deep learning framework for image restoration from images degraded by both multiplicative Gamma noise and blur. Unlike conventional deep equilibrium (DEQ) models that…
Statistical inverse learning and -regularization
Abhishake Rastogi, Tatiana A. Bubba, Tapio Helin +1
We study the recovery of sparse functions from finite, noisy, and indirect observations in the framework of statistical inverse learning. The unknown is modeled as an element of $\…
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…
Revisiting DONet: microlocally inspired filters for incomplete-data tomographic reconstructions
Tatiana A. Bubba, Luca Ratti, Andrea Sebastiani
In this paper, we revisit a supervised learning approach based on unrolling, known as DONet, by providing a deeper microlocal interpretation for its theoretical analysis, and ex…
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…
Learned reconstruction methods for inverse problems: sample error estimates
Luca Ratti
Learning-based and data-driven techniques have recently become a subject of primary interest in the field of reconstruction and regularization of inverse problems. Besides the deve…