1 code implementation • EURO Journal on Computational Optimization 2024 • Pasquale Cascarano, Giorgia Franchini, Erich Kobler, Federica Porta, Andrea Sebastiani
To control the variance of the objective's gradients, we use an automatic sample size selection along with a variable metric to precondition the stochastic gradient directions.
1 code implementation • Computational Optimization and Applications 2022 • Pasquale Cascarano, Giorgia Franchini, Erich Kobler, Federica Porta, Andrea Sebastiani
Numerical results demonstrate the robustness with respect to image content, noise levels and hyperparameters of the proposed models on both denoising and deblurring of simulated as well as real natural and medical images.
1 code implementation • 5 Jul 2021 • Pasquale Cascarano, Maria Colomba Comes, Andrea Sebastiani, Arianna Mencattini, Elena Loli Piccolomini, Eugenio Martinelli
In fluorescence microscopy, Single Molecule Localization Microscopy (SMLM) techniques aim at localizing with high precision high density fluorescent molecules by stochastically activating and imaging small subsets of blinking emitters.
1 code implementation • 15 Feb 2021 • Pasquale Cascarano, Elena Loli Piccolomini, Elena Morotti, Andrea Sebastiani
In particular, we consider different schemes encompassing external and internal denoisers as priors, defined on the image gradient domain.
1 code implementation • 23 Sep 2020 • Pasquale Cascarano, Andrea Sebastiani, Maria Colomba Comes, Giorgia Franchini, Federica Porta
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 of training examples is not always feasible.