In recent years, advancements in machine learning (ML) techniques, in particular, deep
learning (DL) methods have gained a lot of momentum in solving inverse imaging problems …

Abstract The CSGM framework (Bora-Jalal-Price-Dimakis' 17) has shown that
deepgenerative priors can be powerful tools for solving inverse problems. However, to date …

Following the success of deep learning in a wide range of applications, neural network-
based machine-learning techniques have received significant interest for accelerating …

Large neural networks excel in many domains, but they are expensive to train and fine-tune.
A popular approach to reduce their compute or memory requirements is to replace dense …

Deep neural networks have emerged as very successful tools for image restoration and
reconstruction tasks. These networks are often trained end-to-end to directly reconstruct an …

Objective. High-resolution magnetic resonance imaging (MRI) can enhance lesion
diagnosis, prognosis, and delineation. However, gradient power and hardware limitations …

Artificial intelligence (AI)–based image enhancement has the potential to reduce scan times
while improving signal-to-noise ratio (SNR) and maintaining spatial resolution. This study …

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 …

Deep learning (DL) has emerged as a powerful tool for accelerated MRI reconstruction, but
these methods often necessitate a database of fully-sampled measurements for training …

Deep learning based image reconstruction methods outperform traditional methods.
However, neural networks suffer from a performance drop when applied to images from a …