Ambiguity in Solving Imaging Inverse Problems with Deep-Learning-Based Operators.

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 known that image deblurring is mathematically modeled as an ill-posed inverse problem and its solution is difficult to approximate when noise affects the data. Really, one limitation of neural networks for deblurring is their sensitivity to noise and other perturbations, which can lead to instability and produce poor reconstructions.

Switched forced SEIRDV compartmental models to monitor COVID-19 spread and immunization in Italy.

This paper presents a new hybrid compartmental model for studying the COVID-19 epidemic evolution in Italy since the beginning of the vaccination campaign started on 2020/12/27 and shows forecasts of the epidemic evolution in Italy in the first six months. The proposed compartmental model subdivides the population into six compartments and extends the SEIRD model proposed in [E.L.

Recursive Deep Prior Video: A super resolution algorithm for time-lapse microscopy of organ-on-chip experiments.

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 underlying biological processes. A high spatial resolution is essential to capture cell dynamics and interactions from recorded experiments by TLM. Unfortunately, due to physical and cost limitations, acquiring high resolution videos is not always possible.

An efficient method for nonnegatively constrained Total Variation-based denoising of medical images corrupted by Poisson noise.

Medical images obtained with emission processes are corrupted by noise of Poisson type. In the paper the denoising problem is modeled in a Bayesian statistical setting by a nonnegatively constrained minimization problem, where the objective function is constituted by a data fitting term, the Kullback-Leibler divergence, plus a regularization term, the Total Variation function, weighted by a regularization parameter. Aim of the paper is to propose an efficient numerical method for the solution of the constrained problem.