1 citations · 1 across the 11 of their papers we have counts for
12 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…
Physics-Guided Flow Matching for CT Image Reconstruction
Davide Evangelista
Deep generative models have recently emerged as powerful priors for solving ill-posed inverse problems in CT, with diffusion-based approaches achieving state-of-the-art reconstruct…
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…
Improving Diffusion Posterior Samplers with Lagged Temporal Corrections for Image Restoration
Davide Evangelista, Elena Morotti, Francesco Pivi +1
Diffusion-based posterior sampling (PS) is a leading framework for imaging inverse problems, combining learned priors with measurement constraints. Yet, its standard formulations r…
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…
A Diffusion-Based Generative Prior Approach to Sparse-view Computed Tomography
Davide Evangelista, Pasquale Cascarano, Elena Loli Piccolomini
The reconstruction of X-rays CT images from sparse or limited-angle geometries is a highly challenging task. The lack of data typically results in artifacts in the reconstructed im…
Series-Parallel and Planar Graphs for Efficient Broadcasting
David Evangelista, Hovhannes A. Harutyunyan, Aram Khanlari
The broadcasting problem concerns the efficient dissemination of information in graphs. In classical broadcasting, a single originator vertex initially has a message to be transmit…
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…
LIP-CAR: contrast agent reduction by a deep learned inverse problem
Davide Bianchi, Sonia Colombo Serra, Davide Evangelista +3
The adoption of contrast agents in medical imaging protocols is crucial for accurate and timely diagnosis. While highly effective and characterized by an excellent safety profile,…
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…
A data-dependent regularization method based on the graph Laplacian
Davide Bianchi, Davide Evangelista, Stefano Aleotti +3
We investigate a variational method for ill-posed problems, named , which embeds a graph Laplacian operator in the regularization term. The novelty of this meth…