6 papers
Consecutive Posterior Fusion for Diffusive Recovery of Unobservable Image Structures
Elena Morotti, Davide Evangelista, Elena Loli Piccolomini
Solving severely ill-posed imaging inverse problems requires recovering image structures that are unobservable or weakly constrained by the measurements. Diffusion models provide e…
D-Score: A Spectral Hidden-State Signal for Hallucination Detection in Large Language Models
Bianca Raimondi, Davide Evangelista, Maurizio Gabbrielli +1
Large Language Models can produce fluent text that is false, unsupported by the available evidence, or inconsistent with information that appears to be internally represented by th…
CLeAN: Continual Learning Adaptive Normalization in Dynamic Environments
Isabella Marasco, Davide Evangelista, Elena Loli Piccolomini +1
Artificial intelligence systems predominantly rely on static data distributions, making them ineffective in dynamic real-world environments, such as cybersecurity, autonomous trans…
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…
Deep Guess acceleration for explainable image reconstruction in sparse-view CT
Elena Loli Piccolomini, Davide Evangelista, Elena Morotti
Sparse-view Computed Tomography (CT) is an emerging protocol designed to reduce X-ray dose radiation in medical imaging. Traditional Filtered Back Projection algorithm reconstructi…
RISING a new framework for few-view tomographic image reconstruction with deep learning
Davide Evangelista, Elena Morotti, Elena Loli Piccolomini
This paper proposes a new two-step procedure for sparse-view tomographic image reconstruction. It is called RISING, since it combines an early-stopped Rapid Iterative Solver with a…