The vast work in Deep Learning (DL) has led to a leap in image denoising research. Most
DL solutions for this task have chosen to put their efforts on the denoiser's architecture while …

In recent years, classical knowledge-driven approaches for inverse problems have been
complemented by data-driven methods exploiting the power of machine and especially …

How can we design neural networks that allow for stable universal approximation of maps
between topologically interesting manifolds? The answer is with a coordinate projection …

Despite its exceptional soft tissue contrast, Magnetic Resonance Imaging (MRI) faces the
challenge of long scanning times compared to other modalities like X-ray radiography …

Likelihood-based generative models are fundamental building blocks for statistical modeling
of structured data. They can be used to synthesize realistic data samples, and the likelihood …

In recent years, classical knowledge-driven approaches for inverse problems have been
complemented by data-driven methods exploiting the power of machine and especially …