59 citations · 60 across the 10 of their papers we have counts for
3 papers · 1 filter
RELD: Regularization by Latent Diffusion Models for Image Restoration
Pasquale Cascarano, Lorenzo Stacchio, Andrea Sebastiani +3
In recent years, Diffusion Models have become the new state-of-the-art in deep generative modeling, ending the long-time dominance of Generative Adversarial Networks. Inspired by t…
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…
Adaptive Weighted Total Variation boosted by learning techniques in few-view tomographic imaging
Elena Morotti, Davide Evangelista, Andrea Sebastiani +1
This study presents the development of a spatially adaptive weighting strategy for Total Variation regularization, aimed at addressing under-determined linear inverse problems. The…