59 citations · 60 across the 10 of their papers we have counts for
10 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 forward-backward methods for solving non-convex and non-separable composite optimization problems, and specialize them to imag…
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…
Bilevel learning of regularization models and their discretization for image deblurring and super-resolution
Tatiana A. Bubba, Luca Calatroni, Ambra Catozzi +6
Bilevel learning is a powerful optimization technique that has extensively been employed in recent years to bridge the world of model-driven variational approaches with data-driven…
DeepCEL0 for 2D Single Molecule Localization in Fluorescence Microscopy
Pasquale Cascarano, Maria Colomba Comes, Andrea Sebastiani +3
In fluorescence microscopy, Single Molecule Localization Microscopy (SMLM) techniques aim at localizing with high precision high density fluorescent molecules by stochastically act…
Plug-and-Play gradient-based denoisers applied to CT image enhancement
Pasquale Cascarano, Elena Loli Piccolomini, Elena Morotti +1
Blur and noise corrupting Computed Tomography (CT) images can hide or distort small but important details, negatively affecting the diagnosis. In this paper, we present a novel gra…
Combining Weighted Total Variation and Deep Image Prior for natural and medical image restoration via ADMM
Pasquale Cascarano, Andrea Sebastiani, Maria Colomba Comes +2
In the last decades, unsupervised deep learning based methods have caught researchers attention, since in many real applications, such as medical imaging, collecting a great amount…