Compressive sensing enables the reconstruction of high-resolution signals from under-sampled data. While compressive methods simplify data acquisition, they require the solution of difficult recovery problems to make use of the resulting measurements. This article presents a new sensing framework that combines the advantages of both conventional and compressive sensing. Using the proposed STOne...

A Bayesian compressive sensing framework is developed for video reconstruction based on the color coded aperture compressive temporal imaging (CACTI) system. By exploiting the three dimension (3D) tree structure of the wavelet and Discrete Cosine Transformation (DCT) coefficients, a Bayesian compressive sensing inversion algorithm is derived to reconstruct (up to 22) color video frames from a s...

Compressive sensing can minimize the collection of redundant data in the acquisition step. However, it requires a huge amount of storage and creates a tremendous computation burden due to the size of random measurement matrix in compressive sensing theory for big data collection. The separable compressive sensing theory uses two-dimensional separable random measurement matrixes instead of a hug...

Compressive sensing is a methodology for the reconstruction of sparse or compressible signals using far fewer samples than required by the Nyquist criterion. However, many of the results in compressive sensing concern random sampling matrices such as Gaussian and Bernoulli matrices. In common physically feasible signal acquisition and reconstruction scenarios such as superresolution of images, ...

Compressive sensing is a methodology for the reconstruction of sparse or compressible signals using far fewer samples than required by the Nyquist criterion. However, many of the results in compressive sensing concern random sampling matrices such as Gaussian and Bernoulli matrices. In common physically feasible signal acquisition and reconstruction scenarios such as super-resolution of images,...

Compressive sensing is an emerging research field that has applications in signal processing, error correction, medical imaging, seismology, and many more other areas. Compressive sensing has a wide range of applications that include error correction, imaging, radar and many more. We present a new algorithm (the Modified Orthogonal Matching) for signal reconstruction in compressive sensing. We ...

In context aware wireless multimedia sensor networks, scenarios are usually such that signals of multiple distributed sensors contain a common sparse component and each individual signal owns an innovation sparse component. So distributed compressive sensing based on joint sparsity of a signal ensemble concept exploits both these intraand intersignal correlation structures and compress signals ...

Compressive sensing is an emerging method for signal acquisition in which the number of samples ensuring exact reconstruction of the signal to be acquired is far less than the one in the conventional Nyquist sampling approach. In compressive sensing, the signal is acquired by means of few linear non-adaptive measurements, and then reconstructed by finding the sparsest solution via an l1-minimiz...

A new method, Compressive Re-Sampling (CRS), is introduced to reduce the effect of speckle noise, a granular noise inherent in all coherent imaging technologies. The new method is motivated by the successful applications of compressive sensing (CS) to image processing and wireless communications. While compressive sampling is focused on acquiring signals at reduced data rates or reduced acquisi...

Wireless Sensor Network (WSN) is wildly used for a range of applications, one of the most important issues is to improve network lifetime of the sensor node powered by battery. Inspired by Compressive Sensing theory, we proposed an energy-balanced scheme of data gathering denoted by Changeable Probability Compressive Sensing (CPCS). In the proposed approach, we use Compressive Sensing to reduce...