Deep learning (DL) has had unprecedented success and is now entering scientific
computing with full force. However, current DL methods typically suffer from instability, even …

This paper presents a framework for compressed sensing that bridges a gap between
existing theory and the current use of compressed sensing in many real-world applications …

Magnetic resonance imaging (MRI) is a versatile imaging technique that allows different
contrasts depending on the acquisition parameters. Many clinical imaging studies acquire …

Purpose To integrate, optimize, and evaluate a three‐dimensional (3D) contrast‐enhanced
sparse MRA technique with iterative reconstruction on a standard clinical MR system …

In compressed sensing, it is often desirable to consider signals possessing additional
structure beyond sparsity. One such structured signal model–which forms the focus of this …

The purpose of this paper is twofold. The first is to point out that the property of uniform
recovery, meaning that all sparse vectors are recovered, does not hold in many applications …

In this paper we study the general reconstruction of a compactly supported function from its
Fourier coefficients using compactly supported shearlet systems. We assume that only …

The purpose of this paper is twofold. The first is to point out that the Restricted Isometry
Property (RIP) does not hold in many applications where compressed sensing is …

Many of the applications of compressed sensing have been based on variable density
sampling, where certain sections of the sampling coefficients are sampled more densely …

Penalized least-squares iterative image reconstruction algorithms used for spatial resolution-
limited imaging, such as functional magnetic resonance imaging (fMRI), commonly use a …