6 papers
Plug-and-Play blind super-resolution of real MRI images for improved multiple sclerosis diagnosis
Matteo Cannas, Alice Mariottini, Luca Massacesi +3
Magnetic resonance imaging (MRI) is central to the diagnosis of multiple sclerosis, where the identification of biomarkers such as the central vein sign benefits from high-resoluti…
Block-coordinate Plug-And-Play Methods with Armijo-like line-search for Image Restoration
Federica Porta, Simone Rebegoldi, Andrea Sebastiani
In this paper, we develop a class of block-coordinate Plug-and-Play (PnP) methods to address imaging inverse problems. The block-coordinate strategy is designed to reduce the high…
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…
Space-Variant Total Variation boosted by learning techniques in few-view tomographic imaging
Elena Morotti, Davide Evangelista, Andrea Sebastiani +1
This paper focuses on the development of a space-variant regularization model for solving an under-determined linear inverse problem. The case study is a medical image reconstructi…