In recent decades, rapid growth in wireless communication service makes the limited spectrum resources become increasingly scarce. Cognitive radio [1] can solve this problem by dynamic spectrum access technology. Spectrum sensing is one of the key technology of cognitive radios and the quick and accurate perception of broadband spectrum hole is the main challenge. In order to improve the effici...

Introduction. High Angular Resolution Diffusion Imaging (HARDI) has been proposed to avoid the limitations of the conventional Diffusion Tensor Imaging (DTI) and to better explore white matter micro-structure non-invasively. However, HARDI methods normally require many more samples than DTI. For example, Diffusion Spectrum Imaging (DSI), estimates the diffusion Ensemble Average Propagator (EAP)...

Compressed sensing (CS) demonstrates that sparse signals can be recovered from underdetermined linear measurements. We focus on the joint sparse recovery problem where multiple signals share the same common sparse support sets, and they are measured through the same sensing matrix. Leveraging a recent information theoretic characterization of single signal CS, we formulate the optimal minimum m...

Magnetic Resonance Imaging (MRI) is a noninvasive imaging method widely used in medical diagnosis. Data in MRI are obtained line-by-line within the K-space, where there are usually a great number of such lines. For this reason, magnetic resonance imaging is slow. MRI can be accelerated through several methods such as parallel imaging and compressed sensing, where a fraction of the K-space lines...

Image super resolution reconstruction has important significance in remote sensing image feature extraction and classification etc.. Because the remote sensing image size is larger, it is difficult to super resolution reconstruction using multiple images, the compressed sensing (CS) theory was introduced into the super-resolution reconstruction. Algorithm designed the low pass filter to reduce ...

We provide a scheme for exploring the reconstruction limits of compressed sensing by minimizing the general cost function under the random measurement constraints for generic correlated signal sources. Our scheme is based on the statistical mechanical replica method for dealing with random systems. As a simple but non-trivial example, we apply the scheme to a sparse autoregressive model, where ...

Introduction: Sparsity is an essential condition for compressed sensing (CS). Conventional CS-based MRI method relies on finding a good sparsifying transform in order to produce high quality CS-based reconstructions [1]. If sufficient sparsity cannot be achieved, CS-based reconstructions from reduced samples will typically contain artifacts. In this work, we aim at further improving signal spar...

Background While SSFP CMR is the gold standard for assessing left ventricular (LV) function, it requires a regular cardiac rhythm and frequent breath-holds and not all patients with cardiovascular disease are able to achieve this. It is known that Compressed Sensing (CS) greatly reduces data acquisition time however its accuracy for LV volume and mass is currently unknown. This study compares v...

Compressed sensing (CS) exploits the sparsity of a signal in order to integrate acquisition and compression. CS theory enables exact reconstruction of a sparse signal from relatively few linear measurements via a suitable nonlinear minimization process. Conventional CS theory relies on vectorial data representation, which results in good compression ratios at the expense of increased computatio...

This work reveals an experimental microscopy acquisition scheme successfully combining compressed sensing (CS) and digital holography in off-axis and frequency-shifting conditions. CS is a recent data acquisition theory involving signal reconstruction from randomly undersampled measurements, exploiting the fact that most images present some compact structure and redundancy. We propose a genuine...