44 citations · 47 across the 13 of their papers we have counts for
14 papers
Frugality in second-order optimization: floating-point approximations for Newton's method
Giuseppe Carrino, Elena Loli Piccolomini, Elisa Riccietti +1
Minimizing loss functions is central to machine-learning training. Although first-order methods dominate practical applications, higher-order techniques such as Newton's method can…
Blind Restoration of High-Resolution Ultrasound Video
Chu Chen, Kangning Cui, Pasquale Cascarano +3
Ultrasound imaging is widely applied in clinical practice, yet ultrasound videos often suffer from low signal-to-noise ratios (SNR) and limited resolutions, posing challenges for d…
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…
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…
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…
Recursive Deep Prior Video: a Super Resolution algorithm for Time-Lapse Microscopy of organ-on-chip experiments
Pasquale Cascarano, Maria Colomba Comes, Arianna Mencattini +3
Biological experiments based on organ-on-chips (OOCs) exploit light Time-Lapse Microscopy (TLM) for a direct observation of cell movement that is an observable signature of underly…
Efficient gradient-based Super Resolution for simplified image segmentation
Pasquale Cascarano, Luca Calatroni, Elena Loli Piccolomini
We consider a variational model for single-image super-resolution based on the assumption that the gradient of the target image is sparse. We enforce this assumption by considering…
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…
Preliminary analysis of COVID-19 spread in Italy with an adaptive SEIRD model
Elena Loli Piccolomini, Fabiana Zama
In this paper we propose a Susceptible-Infected-Exposed-Recovered-Dead (SEIRD) differential model for the analysis and forecast of the COVID-19 spread in some regions of Italy, usi…
A Fast Splitting Method for efficient Split Bregman Iterations
D. Lazzaro, E. Loli Piccolomini, F. Zama
In this paper we propose a new fast splitting algorithm to solve the Weighted Split Bregman minimization problem in the backward step of an accelerated Forward-Backward algorithm.…
A fast nonconvex Compressed Sensing algorithm for highly low-sampled MR images reconstruction
Damiana Lazzaro, Elena Loli Piccolomini, Fabiana Zama
In this paper we present a fast and efficient method for the reconstruction of Magnetic Resonance Images (MRI) from severely under-sampled data. From the Compressed Sensing theory…