This paper provides a compressive sensing (CS) method of denoising images using Bayesian framework. Some images, for example like magnetic resonance images (MRI) are usually very weak due to the presence of noise and due to the weak nature of the signal itself. So denoising boosts the true signal strength. Under Bayesian framework, we have used two different priors: sparsity and clusterdness in...

In this study, a fast gradient-based compressive sensing (FGB-CS) for noise image and video is proposed. Given a noise image or video, the authors first make it sparse by orthogonal transformation, and then reconstruct it by solving a convex optimisation problem with a novel gradient-based method. The main contribution is twofold. Firstly, they deal with the noise signal reconstruction as a con...

The EEG for use in augmented cognition produces large amounts of compressible data from multiple electrodes mounted on the scalp. This huge amount of data needs to be processed, stored and transmitted and consumes large amounts of power. In turn this leads to physically large EEG units with limited lifetimes which limit the ease of use, and robustness and reliability of the recording. This work...

Compressive sensing is a technique for efficiently acquiring and reconstructing the data. This technique takes advantage of sparseness or compressibility of the data, allowing the entire measured data to be recovered from relatively few measurements. Considering the fact that the BRDF data often can be highly sparse, we propose to employ the compressive sensing technique for an efficient recons...

stepped frequency radars attract many attentions due to their high range resolution. conventional processing technique in these radars is based on the idft. in detecting moving targets, the idft has the problems of range shifting and range spreading, which result in degrading target amplitude, loss of range accuracy and range resolution. to overcome these problems, the velocity compensation met...

MRI (magnetic resonance imaging) is a widely used medical imaging modality that creates an image from scanned data that are essentially the Fourier coefficients of this image. A typical abdominal scan may take around 90 minutes. Can we reduce this time to 30 minutes by using one third of the Fourier coefficients, while maintaining image quality? In this article, we hope to convince the reader t...

In recent years compressive sensing (CS) has been successfully introduced in digital holography (DH). Depending on the ability to sparsely represent an object, the CS paradigm provides an accurate object reconstruction framework from a relatively small number of encoded signal samples. DH has proven to be an efficient and physically realizable sensing modality that can exploit the benefits of C...

In this paper, the theoretical analysis of compressive sensing via random filter, firstly outlined by J. Romberg [compressive sensing by random convolution, submitted to SIAM Journal on Imaging Science on July 9, 2008], has been refined or generalized to the design of general random filter used for compressive sensing. This universal CS measurement consists of two parts: one is from the convolu...

We consider the problem of recovering sparse correlated data on networks. To improve accuracy and reduce costs, it is strongly desirable to take the potentially useful side-information of network structure into consideration. In this paper we present a novel correlated compressive sensing method called CorrCS for networked data. By naturally extending Bayesian compressive sensing, we extract co...