Compressed sensing (CS) has received much attention in wide application recently. As complex sinusoids signal model is widely used in application, research of CS for complex sinusoids is very important. However parameter discretization brings off-gird problem in compressed sensing, which makes its performance degrade significantly. In 2011, Y. Chi studied the sensitivity of Basis Pursuit (BP) a...

In compressed sensing (CS) framework, a signal is sampled below Nyquist rate, and the acquired compressed samples are generally random in nature. However, for efficient estimation of the actual signal, the sensing matrix must preserve the relative distances among the acquired compressed samples. Provided this condition is fulfilled, we show that CS samples will preserve the envelope of the actu...

Compressed sensing (CS) theory assures us that we can accurately reconstruct magnetic resonance images using fewer k-space measurements than the Nyquist sampling rate requires. In traditional CS-MRI inversion methods, the fact that the energy within the Fourier measurement domain is distributed non-uniformly is often neglected during reconstruction. As a result, more densely sampled low-frequen...

Compressed Sensing (CS) seeks to recover an unknown vector with N entries by making far fewer than N measurements; it posits that the number of compressed sensing measurements should be comparable to the information content of the vector, not simply N . CS combines the important task of compression directly with the measurement task. Since its introduction in 2004 there have been hundreds of ma...

Compressed sensing (CS) is a signal processing framework for efficiently reconstructing a signal from a small number of measurements, obtained by linear projections of the signal. Block-based CS is a lightweight CS approach that is mostly suitable for processing very high-dimensional images and videos: it operates on local patches, employs a low-complexity reconstruction operator and requires s...

We consider compressed acquisition and progressive reconstruction of spatially and temporally correlated data in wireless sensor networks (WSNs). We propose a novel, sliding window based compressed sensing (CS) method in which the sink can instantaneously reconstruct WSN samples from periodically acquired CS measurements. Moreover, the prior information attained by decoding the WSN readings mul...

Compressed Sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. It is promising that CS can be utilized in environments where the signal acquisition process is extremely difficult or costly, e.g., a resource-constrained environment like the smartphone platform, or a band-limited...

Dynamic tracking of sparse targets has been one of the important topics in array signal processing. Recently, compressed sensing (CS) approaches have been extensively investigated as a new tool for this problem using partial support information obtained by exploiting temporal redundancy. However, most of these approaches are formulated under single measurement vector compressed sensing (SMV-CS)...

Magnetic resonance imaging has been benefited from compressed sensing in improving imaging speed. But the computation time of compressed sensing magnetic resonance imaging (CS-MRI) is relatively long due to its iterative reconstruction process. Recently, a patch-based nonlocal operator (PANO) has been applied in CS-MRI to significantly reduce the reconstruction error by making use of self-simil...

Integrated Services Digital Broadcasting for Terrestrial (ISDB-T) One-Seg is a Japanese standard for digital television specifically for mobile reception. It uses Orthogonal Frequency Division Multiplexing (OFDM) that provides robustness against multipath fading. A novel approach called Compressed Sensing (CS) has been implemented for estimating the Channel State Information (CSI). The CS impro...