6 papers
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 $\…
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…
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.…
Fast Inexact Bilevel Optimization for Analytical Deep Image Priors
Mohammad Sadegh Salehi, Tatiana A. Bubba, Yury Korolev
The analytical deep image prior (ADP) introduced by Dittmer et al. (2020) establishes a link between deep image priors and classical regularization theory via bilevel optimization.…
TomoSelfDEQ: Self-Supervised Deep Equilibrium Learning for Sparse-Angle CT Reconstruction
Tatiana A. Bubba, Matteo Santacesaria, Andrea Sebastiani
Deep learning has emerged as a powerful tool for solving inverse problems in imaging, including computed tomography (CT). However, most approaches require paired training data with…
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 e…