We propose a non-stationary iterated network Tikhonov (iNETT) method for the solution of ill-
posed inverse problems. The iNETT employs deep neural networks to build a data-driven …

Image reconstruction problems, like image deblurring and computer tomography, are
usually ill-posed and require regularization. A popular approach to regularization is to …

Images that have been contaminated by various kinds of blur and noise can be restored by
the minimization of an ℓ p-ℓ q functional. The quality of the reconstruction depends on the …

In imaging problems, the graph Laplacian is proven to be a very effective regularization
operator when a good approximation of the image to restore is available. In this paper, we …

Uncovering acquired drug resistance mechanisms has garnered considerable attention as
drug resistance leads to treatment failure and death in patients with cancer. Although …

Given a compact linear operator K, the (pseudo) inverse K^\dagger is usually substituted by
a family of regularizing operators R_α which depends on K itself. Naturally, in the actual …

Deep learning techniques have shown significant potential in many applications through
recent years. The achieved results often outperform traditional techniques. However, the …

We investigate a variational method for ill-posed problems, named $\texttt {graphLa+}\Psi $,
which embeds a graph Laplacian operator in the regularization term. The novelty of this …

The use of the Laplacian of a properly constructed graph for denoising images has attracted
a lot of attention in the last years. Recently, a way to use this instrument for image deblurring …

The Arnoldi-Tikhonov method is a well-established regularization technique for solving large-
scale ill-posed linear inverse problems. This method leverages the Arnoldi decomposition to …