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In this paper, we propose a new framework for compressive video sensing (CVS) that exploits the inherent spatial and temporal redundancies of a video sequence, effectively. The proposed method splits the video sequence into the key and non-key frames followed by dividing each frame into the small non-overlapping blocks of equal sizes. At the decoder side, the key frames are reconstructed using adaptively...
Based on the concept of sparse Bayesian learning, an expectation and maximization algorithm is proposed for cooperative spectrum sensing and locationing of the primary transmitters in cognitive radio systems. Different from typical approaches, not only the signal strength, but also the number and the radio power profiles of the primary transmitters are estimated, which greatly facilitates resource...
A robust signal recovery approach for compressive sensing using unconstrained minimization is proposed. The ℓ1 penalty function of the constrained ℓ1-regularized least-squares recovery problem is replaced by the smoothly clipped absolute deviation (SCAD) sparsity-promoting penalty function. A convex and differentiable local quadratic approximation for the SCAD function is employed to render the computation...
In this paper, following the Compressed Sensing (CS) paradigm, we study the problem of recovering sparse or compressible signals from uniformly quantized measurements. We present a new class of convex optimization programs, or decoders, coined Basis Pursuit DeQuantizer of moment p (BPDQp), that model the quantization distortion more faithfully than the commonly used Basis Pursuit DeNoise (BPDN) program...
Perceived from the definition of compressed sensing (CS), the sparser the signal is, the better the recovery will be. Meanwhile, the third-generation wavelet-contourlet is able to sparsely represent signals and detect the singularity of smooth curve. Taking into account the mixed noise from random projection of CS model, we are trying to carry out the following: let the image transformed into contourlet...
The observations in many applications consist of counts of discrete events, such as photons hitting a detector, which cannot be effectively modeled using an additive bounded or Gaussian noise model, and instead require a Poisson noise model. As a result, accurate reconstruction of a spatially or temporally distributed phenomenon (f) from Poisson data (y) cannot be accomplished by minimizing a conventional...
It has been known for a while that lscr1-norm relaxation can in certain cases solve an under-determined system of linear equations. Recently, proved (in a large dimensional and statistical context) that if the number of equations (measurements in the compressed sensing terminology) in the system is proportional to the length of the unknown vector then there is a sparsity (number of non-zero elements...
It has been known for a while that l1-norm relaxation can in certain cases solve an under-determined system of linear equations. Recently, [5, 10] proved (in a large dimensional and statistical context) that if the number of equations (measurements in the compressed sensing terminology) in the system is proportional to the length of the unknown vector then there is a sparsity (number of non-zero elements...
Sparse Bayesian Learning (SBL) has been used as a sparse signal recovery algorithm for compressed sensing. It has been shown that SBL is easy to use and can recover sparse signals more accurately than the l1 based optimization approaches, which require a delicate choice of user parameters. We propose herein a modified Expectation Maximization (EM) based SBL algorithm referred to as SBL-alpha and...
Recent studies have shown that sparse representation can be used effectively as a prior in linear inverse problems. However, in many multiscale bases (e.g., wavelets), signals of interest (e.g., piecewise-smooth signals) not only have few significant coefficients, but also those significant coefficients are well-organized in trees. We propose to exploit this, named sparse-tree, prior for linear inverse...
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