Compressed sensing (CS) is a rapidly growing field, attracting considerable attention in many areas from imaging to communication and control systems. This signal processing framework is based on the reconstruction of signals, which are sparse in some domain, from a very small data collection of linear projections of the signal. The solution to the underdetermined linear system, resulting from ...

Fig 3: Comparison of BLS-GSM CS and l1 norm CS for imaging of right coronary artery. Fig. 1: a) Wavelet coefficients of a 2D slice of a coronary image. b) Random permutation of the same coefficients shown in (a). Both data have equivalent lp norm, which suggests CS lp norm regularizers do not take into account the clustering and correlation of information in the transform domain. Compressed Sen...

In this paper we are concerned with the solution of Compressed Sensing (CS) problems where the signals to be recovered are sparse in coherent and redundant dictionaries. We extend the primal-dual Newton Conjugate Gradients (pdNCG) method in [11] for CS problems. We provide an inexpensive and provably effective preconditioning technique for linear systems using pdNCG. Numerical results are prese...

Background Compressed Sensing (CS) is a theory with potential to reconstruct sparse images from a small number of random acquisitions. Particularly in MRI, CS aims to reconstruct the image from incomplete K-space data with minimum penalty on the image quality. The image is recovered from the sub-sampled K-space data, using image sparsity in a known sparse transform domain. Cardiac MRI has a spa...

Introduction: Compressed Sensing (CS) has been demonstrated to reconstruct sparse MR images of adequate quality from highly undersampled data [1], resulting in reduced scan times. In MRI, extensive motion during the acquisition (e.g. respiratory motion in cardiac scans) can cause inconsistencies in the k-space data, introducing blurring and ghosting like motion artefacts in the reconstructed im...

Compressed sensing (CS) schemes are proposed for monostatic as well as synthetic aperture radar (SAR) imaging with chirps. In particular, a simple method is developed to improve performance with off-grid targets. Tomographic formulation of spotlight SAR is analyzed by CS methods with several bases and under various bandwidth constraints. Performance guarantees are established via coherence boun...

Compressed sensing has shown great potential in reducing data acquisition time in magnetic resonance imaging (MRI). Recently, a spread spectrum compressed sensing MRI method modulates an image with a quadratic phase. It performs better than the conventional compressed sensing MRI with variable density sampling, since the coherence between the sensing and sparsity bases are reduced. However, spr...

Compressed sensing (CS) is a new signal acquisition paradigm that enables the reconstruction of signals and images from a low number of samples. A particularly exciting application of CS is Magnetic Resonance Imaging (MRI), where CS significantly speeds up scan time by requiring far fewer measurements than standard MRI techniques. Such a reduction in sampling time leads to less power consumptio...

Distributed compressed sensing is the extension of compressed sampling (CS) to sensor networks. The idea is to design a CS joint decoding scheme at a central decoder (base station) that exploits the inter-sensor correlations, in order to recover the whole observations from very few number of random measurements per node. In this paper, we focus on modeling the correlations and on the design and...

The propagations of 60 GHz millimeter-wave system, which occupies an enormous operation bandwidth, are always known to be intensively dispersive. This may, in practice, pose great challenges to the estimation of channel state information. In this article, we investigated a promising compressed sensing (CS) algorithm and its practical applications in the channel estimations of emerging 60 GHz mi...