The promise of compressive sensing, exploitation of compressibility to achieve high quality
image reconstructions with less data, has attracted a great deal of attention in the medical …

Abstract In Compressive Sensing Magnetic Resonance Imaging (CS-MRI), one can
reconstruct a MR image with good quality from only a small number of measurements. This …

In many signal processing applications, one wishes to acquire images that are sparse in
transform domains such as spatial finite differences or wavelets using frequency domain …

Incoherence between sparsity basis and sensing basis is an essential concept for
compressive sampling. In this context, we advocate a coherence-driven optimization …

The Shannon/Nyquist sampling theorem tells us that in order to not lose information when
uniformly sampling a signal we must sample at least two times faster than its bandwidth. In …

We consider the group basis pursuit problem, which extends basis pursuit by replacing the 1
norm with a weighted-2,1 norm. We provide an anytime algorithm, called generalized …

Nowadays, a large amount of information has to be transmitted or processed. This implies
high-power processing, large memory density, and increased energy consumption. In …

Compressed sensing (CS) has recently drawn great attentions in the MRI research
community. The most desirable property of CS in MRI application is that it allows sampling of …

In addition, we present a new algorithm (the Modified Frame Reconstruction or MFR
algorithm) for signal reconstruction in compressive sensing. This algorithm generalises …

We present a novel sparse signal reconstruction method, iterative support detection (ISD),
aiming to achieve fast reconstruction and a reduced requirement on the number of …