1 citations · 2 across the 12 of their papers we have counts for
14 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…
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…
A Line--Search--Based Stochastic Gradient Method for 3D Computed Tomography
Tatiana A. Bubba, Elena Morotti, Federica Porta +2
We introduce FB-LISA, a forward-backward (FB) generalization of a recently proposed line-search-based stochastic gradient algorithm to address the imaging problem of volumetric rec…
An incremental algorithm for non-convex AI-enhanced medical image processing
Elena Morotti
Solving non-convex regularized inverse problems is challenging due to their complex optimization landscapes and multiple local minima. However, these models remain widely studied a…
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…
Deep image prior inpainting of ancient frescoes in the Mediterranean Alpine arc
Fabio Merizzi, Perrine Saillard, Oceane Acquier +4
The unprecedented success of image reconstruction approaches based on deep neural networks has revolutionised both the processing and the analysis paradigms in several applied disc…
Ambiguity in solving imaging inverse problems with deep learning based operators
Davide Evangelista, Elena Morotti, Elena Loli Piccolomini +1
In recent years, large convolutional neural networks have been widely used as tools for image deblurring, because of their ability in restoring images very precisely. It is well kn…
To be or not to be stable, that is the question: understanding neural networks for inverse problems
Davide Evangelista, James Nagy, Elena Morotti +1
The solution of linear inverse problems arising, for example, in signal and image processing is a challenging problem since the ill-conditioning amplifies, in the solution, the noi…
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…
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…
Optimization methods for very accurate Digital Breast Tomosynthesis image reconstruction
Elena Morotti, Elena Loli Piccolomini
Digital Breast Tomosynthesis is an X-ray imaging technique that allows a volumetric reconstruction of the breast, from a small number of low-dose two-dimensional projections. Altho…