Inverse problems with Poisson data arise in many photonic imaging modalities in medicine,
engineering and astronomy. The design of regularization methods and estimators for such …

Computed Tomography (CT) is an essential imaging tool for medical inspection, diagnosis
and prevention. While X-rays CT is a consolidated technology, there is nowadays a strong …

Abstract Deep Image Prior (DIP) is currently among the most efficient unsupervised deep
learning based methods for ill-posed inverse problems in imaging. This novel framework …

The aim of this paper is to deepen the convergence analysis of the scaled gradient
projection (SGP) method, proposed by Bonettini et al in a recent paper for constrained …

In this paper, a methodology is investigated for signal recovery in the presence of non-
Gaussian noise. In contrast with regularized minimization approaches often adopted in the …

Image segmentation is an essential but critical component in low level vision, image
analysis, pattern recognition, and now in robotic systems. In addition, it is one of the most …

The physical process underlying microscopy imaging suffers from several issues: some of
them include the blurring effect due to the Point Spread Function, the presence of Gaussian …

Regularization by Denoising (RED) is a well-known method for solving image restoration
problems by using learned image denoisers as priors. Since the regularization parameter in …

In this paper we address the problem of automatically selecting the regularization parameter
in variational models for the restoration of images corrupted by Poisson noise. More …

The effectiveness of variational methods for restoring images corrupted by Poisson noise
strongly depends on the suitable selection of the regularization parameter balancing the …