We propose a new variational model in Sobolev–Orlicz spaces with non-standard growth
conditions of the objective functional and discuss its applications to image processing. The …

Recently, we have witnessed the success of total variation (TV) for many imaging
applications. However, traditional TV is defined on the original pixel domain, which limits its …

Theoretical guarantees for the robust solution of inverse problems have important
implications for applications. To achieve both guarantees and high reconstruction quality …

In this paper, we address the image denoising problem in presence of speckle degradation
typically arising in ultra-sound images. Variational methods and Convolutional Neural …

In the last years, the design of image reconstruction methods in the field of quantitative
Magnetic Resonance Imaging (qMRI) has experienced a paradigm shift. Often, when …

In this work, we extend the residual whiteness principle, originally proposed in (Lanza et al.
in Electron Trans Numer Anal 53: 329–352 2020) for the selection of a single regularization …

We propose to enhance 1-weakly convex ridge regularizers for image reconstruction by
incorporating spatial adaptivity. To this end, we resort to a neural network that generates a …

We consider an unsupervised bilevel optimization strategy for learning regularization
parameters in the context of imaging inverse problems in the presence of additive white …

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 …

State-of-the-art image reconstruction often relies on complex, highly parameterized deep
architectures. We propose an alternative: a data-driven reconstruction method inspired by …