we give some new results on sparse signal recovery in the presence of noise, forweighted spaces. traditionally, were used dictionaries that have the norm equal to 1, but, forrandom dictionaries this condition is rarely satised. moreover, we give better estimationsthen the ones given recently by cai, wang and xu.

Many practical compressible signals like image signals or the networked data in wireless sensor networks have non-uniform support distribution in their sparse representation domain. Utilizing this prior information, a novel compressed sensing (CS) scheme with unequal protection capability is proposed in this paper by introducing a windowing strategy called expanding window compressed sensing (E...

speckle is a granular disturbance in coherent images such as synthetic aperture radar (sar) images, modeled as a multiplicative noise. this noise degrades the sar image and complicates the image exploitation using automated image analysis techniques. several approaches have been developed to reduce the effect of speckle noise. recently, the application of compressed sensing (cs) is explored in ...

Background Compressed sensing (CS) is an efficient tool that accelerates the data acquisition in MRI through the significant reduction of required measurements for image reconstruction. In recent years, there have been significant interests in the use of Compressed Sensing (CS) in Dynamic applications [1]. Since Cine cardiac images, as a dynamic data, has both spatial and temporal sparsity, it ...

Both parallel MRI and compressed sensing (CS) are emerging techniques to accelerate conventional MRI by reducing the number of acquired data. The combination of parallel MRI and CS for further acceleration is of great interest. In this paper, we propose a novel method to combine sensitivity encoding (SENSE), one of the standard methods for parallel MRI, and compressed sensing for rapid MR imagi...

In this project, we apply compressed sensing (CS) technique to achieve high-spatiotemporal functional magnetic resonance imaging (MRI), which is very challenging with conventional approaches due to physical limitations such as slew rate. We also use its ideas of exploiting sparsity for image denoising. Keywords—compressed sensing; MRI; sparsity; image denoising

Compressed Sensing based Terahertz imaging (CS-THz) is a computational imaging technique. It uses only one THz receiver to accumulate the random modulated image measurements where the original THz image is reconstruct from these measurements using compressed sensing solvers. The advantage of the CS-THz is its reduced acquisition time compared with the raster scan mode. However, when it applied ...

In the classical Shannon/Nyquist sampling theorem, information is not lost in uniformly sampling a signal, signal must be sampled at least two times faster than its bandwidth. Because of the restriction of the Nyquist rate, it end up with too many samples in many applications, and it becomes a great challenge for further transmission and storage. In recent years, an emerging theory of signal ac...

Undersampling k-space data is an efficient way to speed up the magnetic resonance imaging (MRI) process. As a newly developed mathematical framework of signal sampling and recovery, compressed sensing (CS) allows signal acquisition using fewer samples than what is specified by Nyquist-Shannon sampling theorem whenever the signal is sparse. As a result, CS has great potential in reducing data ac...

The performance of compressed sensing (CS) algorithms is dependent on the sparsity level of the underlying signal, the type of sampling pattern used and the reconstruction method applied. The higher the incoherence of the sampling pattern used for undersampling, less aliasing will be noticeable in the aliased signal space, resulting in better CS reconstruction. In this work, based on point spre...