Computed tomography (CT) is used in a wide range of medical imaging diagnoses.
However, the reconstruction of CT images from raw projection data is inherently complex …

This work is concerned with the following fundamental question in scientific machine
learning: Can deep-learning-based methods solve noise-free inverse problems to near …

Over recent years, deep learning methods have become an increasingly popular choice for
solving tasks from the field of inverse problems. Many of these new data-driven methods …

Recent years have witnessed a growth in mathematics for deep learning—which seeks a
deeper understanding of the concepts of deep learning with mathematics and explores how …

X-ray computed tomography (XCT) has been shown to be a reliable tool for quality
inspection, material evaluation, and dimensional measurement tasks across diverse …

By absorbing the merits of both the model-and data-driven methods, deep physics-engaged
learning scheme achieves high-accuracy and interpretable image reconstruction. It has …

Over recent years, deep learning (DL) has established itself as a powerful tool across a
broad spectrum of domains in imaging—eg, classification, prediction, detection …

We introduce a method for the fast estimation of data-adapted, spatially and temporally
dependent regularization parameter-maps for variational image reconstruction, focusing on …

Deep image prior (DIP) was recently introduced as an effective unsupervised approach for
image restoration tasks. DIP represents the image to be recovered as the output of a deep …

Learning neural networks using only few available information is an important ongoing
research topic with tremendous potential for applications. In this paper, we introduce a …