INTRODUCTION: Compressed Sensing (CS) ([1], [2], [3], [4]) is a recently created algorithm which allows reconstructing a signal from a small portion of its Fourier coefficients if that signal is sparse in a suitable basis. It was first used by Lustig et al. [5] in MRI, and it has become a popular alternative for speeding up the MRI acquisition processes. In practice, CS has been implemented as ...

Compressive sampling (CS), also called Compressed Sensing, has generated a tremendous amount of excitement in the image processing community. It provides an alternative to Shannon/Nyquist sampling when the signal under acquisition is known to be sparse or compressible. In this paper, we propose a new efficient image fusion method for compressed sensing imaging. In this method, we calculate the ...

A novel compressive-sensing based signal multiplexing scheme is proposed in this paper to further improve the multiplexing gain for multiple input multiple output (MIMO) system. At the transmitter side, a Gaussian random measurement matrix in compressive sensing is employed before the traditional spatial multiplexing in order to carry more data streams on the available spatial multiplexing stre...

Super Resolution based on Compressed Sensing (CS) considers low resolution (LR) image patch as the compressive measurement of its corresponding high resolution (HR) patch. In this paper we propose a single image super resolution scheme with compressive K-SVD algorithm(CKSVD) for dictionary learning incorporating fusion of sparse approximation algorithms to produce better results. The CKSVD algo...

PURPOSE Respiratory motion correction remains a challenge in coronary magnetic resonance imaging (MRI) and current techniques, such as navigator gating, suffer from sub-optimal scan efficiency and ease-of-use. To overcome these limitations, an image-based self-navigation technique is proposed that uses "sub-images" and compressed sensing (CS) to obtain translational motion correction in 2D. The...

Compressed sensing (CS) reconstructions of under-sampled measurements generate missing data based on assumptions of image sparsity. Non-contrast time-of-flight MR angiography (TOF-MRA) is a good candidate for CS based acceleration, as MRA images feature bright trees of sparse vessels over a well-suppressed anatomical background signal. A short scan time derived from CS is beneficial for patient...

Introduction: Water-fat separation is of interest in several MRI applications including fat suppression and fat quantification. Chemical shift imaging allows robust water-fat separation [1, 2], however the acquisition of multiple images results in prolonged scan time. Accelerated water-fat separation using compressed sensing (CS) was partly addressed in [3] by considering the separation as a sp...

Magnetic resonance imaging (MRI) is a non-invasive imaging modality, which offers high spatial resolution and excellent soft tissue contrast without employing ionizing radiation. MRI is sensitive to a wide range of contrast mechanisms that allow assessment of both morphology and physiology, making it a modality of choice for many clinical applications. A major limitation of MRI is that data acq...

Introduction: The recently introduced Compressed Sensing (CS) theory promises to accelerate data acquisition in magnetic resonance imaging (MRI) [1-3]. One of the important requirements in CS MRI is that the image has a sparse representation. This sparse representation is crucial for successful recovery in CS. Generally speaking, sparser representations yield improved performance in terms of ei...