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This paper considers the distributed compressed sensing (DCS), where each node has a common component and an innovation component. Most existing reconstruction methods for this DCS model are actually centralized, where the measurements of each signal are utilized together at a certain node. In this paper, we propose a decentralized reconstruction algorithm that works in an iterative manner, where each node implements the reconstruction in each iteration only with its own measurements and some estimations in the previous iteration from other nodes. Simulation results show that the proposed algorithm has better performance than separate recovery. [ABSTRACT FROM AUTHOR]

Compressive sensing provides simultaneous sensing and compression of data. Block compressive sensing (BCS) of images has gained a prominence in recent years due to low encoding complexity. In this paper, we propose the reconstruction algorithm for BCS framework based on iterative re-weighted l 1 norm minimization. In the proposed algorithm, the desired signal sparsity is achieved using l 1 norm minimization while Wiener filtering is incorporated as the smoothing operator. We also propose block based joint reconstruction algorithm for correlated images and video frames. The correlation is captured through the joint sparsity model by minimizing the objective function which promotes the common sparsity structure. The performance of proposed algorithms is tested on different stereo images and video data. Our analysis shows that the proposed individual reconstruction algorithm gives good compression performance as compared to existing schemes. Extensive analysis on correlated images demonstrates that the proposed joint reconstruction algorithm outperforms individual reconstruction algorithms. [ABSTRACT FROM AUTHOR]

With advances in sensing technologies and wireless sensor networks, energy consumption is a key factor for data collection in sensor network. Compressive sensing has attracted more attention for reducing energy cost due to its enabled sub-Nyquist sampling rate. In this paper, the JSM-2 model was introduced for data compressed collection for sensor network based on distribution compressive sensing theory. And we deem that the quantization configuration is also a critical factor for energy efficiency for data communication. Then, we constructed energy consumption configuration model joint distribution compressive sensing and quantization compressive sensing. The experimental results showed that the proposed algorithm can gain more energy performance than conventional QCS architecture for sensor network data collection. [ABSTRACT FROM AUTHOR]

Multi-modal image fusion is necessary for describing a target abundantly. With the consideration of the correlations between multi-source signals and the sparse characteristics of image, this paper proposed a novel fusion rule of multi-modal image fusion scheme based on the joint sparsity model. First, the source image was represented as a shared sparse component and an exclusive sparse component with an over-complete dictionary. Second, the designed novel fusion rule acts on the shared and exclusive sparse coefficients to obtain the fused sparse coefficients. Finally, fused image was reconstructed by the fused sparse coefficients and the dictionary. The proposed approach was tested on the infrared and visual images, medical images. The results were compared with those of traditional methods, such as the multi-scale transform based methods, sparse representation based methods and joint sparsity representation based methods. Experimental results demonstrated that the proposed method outperforms the existing state-of-the-art methods, in terms of better texture clarity. Moreover, the fused image shows better edge consistence and visual effect. [ABSTRACT FROM AUTHOR]

Distributed Compressed Sensing (DCS) extends compressive sampling from single signal to multi-signal ensembles. It also enables joint recovery that exploits both intra- and inter-signal correlation structures. Statistical MIMO radar systems that are made up of widely separated transmit/receive antennas form distributed detection systems for targets among transmitters, targets and receivers. In this paper, DCS is applied to statistical MIMO radars, and through the analysis of sparisty of the delays of target echo signals in the range space, the idea is proposed to construct target scene by joining all received signals. It also establishes the joint sparsity model of received signals, and gives joint reconstruction algorithms that can estimate target parameters. Simulation results show that, compared with the algorithm based on CS, the one based on DCS increases the parameter estimation accuracy while offering a reduction in the number of measurements. It is also validated that DCS -MIMO radars can effectively overcome target RCS fluctuations.

In this letter, a sparse representation-based nearest neighbor (SRNN) classifier is proposed. Unlike the traditional $k$-nearest neighbor (NN) classifier that employs the Euclidean distance as similarity metric, the proposed SRNN considers sparse coefficients to determine the label of testing samples, since sparse coefficients can reflect the similarity between data and provide more discriminative information. A local SRNN (LSRNN) classifier is also proposed to utilize class-specific sparse coefficients to improve the performance. Furthermore, due to the fact that neighboring pixels tend to belong to the same class with high probability, a spatially joint version of LSRNN, called JSRNN, is developed to further improve LSRNN. The proposed SRNN, LSRNN, and JSRNN have been validated on several hyperspectral remote sensing image data sets. Experimental results demonstrate that the proposed classifiers increase the classification accuracy compared with the traditional $k$-NN, local mean-based NN (LMNN) classifiers, and original sparse representation classifiers using representation residuals. [ABSTRACT FROM PUBLISHER]

In the context of spectrum reusing in Cognitive Radio (CR) networks, spectrum availability should be considered with respect to both space and frequency in making the Power Spectral Density (PSD) map concept. For this reason, the sensed PSDs by the distributed sensors in the area are collected and fused by a fusion center (FC). But, for a given zone, the sensed PSD by neighbor CR sensors may contain a shared common component for a while, and this component can be exploited in the theory of the distributed source coding to compress as much as possible. For this reason, distributed compressive sensing (DCS) methods are used here to exploit the correlation between PSDs and compress them more and reduce this type of overhead traffic. The proposed method can be used to develop a framework when the holding time of the users is large in comparison with the rate of the spectrum sensing. [ABSTRACT FROM PUBLISHER]

The compressed sensing (CS) theory makes sample rate relate to signal structure and content. CS samples and compresses the signal with far below Nyquist sampling frequency simultaneously. However, CS only considers the intra-signal correlations, without taking the correlations of the multi-signals into account. Distributed compressed sensing (DCS) is an extension of CS that takes advantage of both the inter- and intra-signal correlations, which is wildly used as a powerful method for the multi-signals sensing and compression in many fields. In this paper, the characteristics and related works of DCS are reviewed. The framework of DCS is introduced. As DCS's main portions, sparse representation, measurement matrix selection, and joint reconstruction are classified and summarized. The applications of DCS are also categorized and discussed. Finally, the conclusion remarks and the further research works are provided. [ABSTRACT FROM AUTHOR]

Many natural signals exhibit a sparse representation, whenever a suitable describing model is given. Here, a linear generative model is considered, where many sparsity-based signal processing techniques rely on such a simplified model. As this model is often unknown for many classes of the signals, we need to select such a model based on the domain knowledge or using some exemplar signals. This paper presents a new exemplar based approach for the linear model (called the dictionary) selection, for such sparse inverse problems. The problem of dictionary selection, which has also been called the dictionary learning in this setting, is first reformulated as a joint sparsity model. The joint sparsity model here differs from the standard joint sparsity model as it considers an overcompleteness in the representation of each signal, within the range of selected subspaces. The new dictionary selection paradigm is examined with some synthetic and realistic simulations. [ABSTRACT FROM PUBLISHER]

Video anomaly detection can be used in the transportation domain to identify unusual patterns such as traffic violations, accidents, unsafe driver behavior, street crime, and other suspicious activities. A common class of approaches relies on object tracking and trajectory analysis. Very recently, sparse reconstruction techniques have been employed in video anomaly detection. The fundamental underlying assumption of these methods is that any new feature representation of a normal/anomalous event can be approximately modeled as a (sparse) linear combination prelabeled feature representations (of previously observed events) in a training dictionary. Sparsity can be a powerful prior on model coefficients but challenges remain in the detection of anomalies involving multiple objects and the ability of the linear sparsity model to effectively allow for class separation. The proposed research addresses both these issues. First, we develop a new joint sparsity model for anomaly detection that enables the detection of joint anomalies involving multiple objects. This extension is highly nontrivial since it leads to a new simultaneous sparsity problem that we solve using a greedy pursuit technique. Second, we introduce nonlinearity into, that is, kernelize. The linear sparsity model to enable superior class separability and hence anomaly detection. We extensively test on several real world video datasets involving both single and multiple object anomalies. Results show marked improvements in detection of anomalies in both supervised and unsupervised scenarios when using the proposed sparsity models. [ABSTRACT FROM AUTHOR]

A significant recent advance in hyperspectral image (HSI) classification relies on the observation that the spectral signature of a pixel can be represented by a sparse linear combination of training spectra from an overcomplete dictionary. A spatiospectral notion of sparsity is further captured by developing a joint sparsity model, wherein spectral signatures of pixels in a local spatial neighborhood (of the pixel of interest) are constrained to be represented by a common collection of training spectra, albeit with different weights. A challenging open problem is to effectively capture the class conditional correlations between these multiple sparse representations corresponding to different pixels in the spatial neighborhood. We propose a probabilistic graphical model framework to explicitly mine the conditional dependences between these distinct sparse features. Our graphical models are synthesized using simple tree structures which can be discriminatively learnt (even with limited training samples) for classification. Experiments on benchmark HSI data sets reveal significant improvements over existing approaches in classification rates as well as robustness to choice of training. [ABSTRACT FROM PUBLISHER]

In this paper, a novel nonlinear technique for hyperspectral image (HSI) classification is proposed. Our approach relies on sparsely representing a test sample in terms of all of the training samples in a feature space induced by a kernel function. For each test pixel in the feature space, a sparse representation vector is obtained by decomposing the test pixel over a training dictionary, also in the same feature space, by using a kernel-based greedy pursuit algorithm. The recovered sparse representation vector is then used directly to determine the class label of the test pixel. Projecting the samples into a high-dimensional feature space and kernelizing the sparse representation improve the data separability between different classes, providing a higher classification accuracy compared to the more conventional linear sparsity-based classification algorithms. Moreover, the spatial coherency across neighboring pixels is also incorporated through a kernelized joint sparsity model, where all of the pixels within a small neighborhood are jointly represented in the feature space by selecting a few common training samples. Kernel greedy optimization algorithms are suggested in this paper to solve the kernel versions of the single-pixel and multi-pixel joint sparsity-based recovery problems. Experimental results on several HSIs show that the proposed technique outperforms the linear sparsity-based classification technique, as well as the classical support vector machines and sparse kernel logistic regression classifiers. [ABSTRACT FROM AUTHOR]

A new sparsity-based algorithm for the classification of hyperspectral imagery is proposed in this paper. The proposed algorithm relies on the observation that a hyperspectral pixel can be sparsely represented by a linear combination of a few training samples from a structured dictionary. The sparse representation of an unknown pixel is expressed as a sparse vector whose nonzero entries correspond to the weights of the selected training samples. The sparse vector is recovered by solving a sparsity-constrained optimization problem, and it can directly determine the class label of the test sample. Two different approaches are proposed to incorporate the contextual information into the sparse recovery optimization problem in order to improve the classification performance. In the first approach, an explicit smoothing constraint is imposed on the problem formulation by forcing the vector Laplacian of the reconstructed image to become zero. In this approach, the reconstructed pixel of interest has similar spectral characteristics to its four nearest neighbors. The second approach is via a joint sparsity model where hyperspectral pixels in a small neighborhood around the test pixel are simultaneously represented by linear combinations of a few common training samples, which are weighted with a different set of coefficients for each pixel. The proposed sparsity-based algorithm is applied to several real hyperspectral images for classification. Experimental results show that our algorithm outperforms the classical supervised classifier support vector machines in most cases. [ABSTRACT FROM PUBLISHER]

This letter proposes a simultaneous joint sparsity model for target detection in hyperspectral imagery (HSI). The key innovative idea here is that hyperspectral pixels within a small neighborhood in the test image can be simultaneously represented by a linear combination of a few common training samples but weighted with a different set of coefficients for each pixel. The joint sparsity model automatically incorporates the interpixel correlation within the HSI by assuming that neighboring pixels usually consist of similar materials. The sparse representations of the neighboring pixels are obtained by simultaneously decomposing the pixels over a given dictionary consisting of training samples of both the target and background classes. The recovered sparse coefficient vectors are then directly used for determining the label of the test pixels. Simulation results show that the proposed algorithm outperforms the classical hyperspectral target detection algorithms, such as the popular spectral matched filters, matched subspace detectors, and adaptive subspace detectors, as well as binary classifiers such as support vector machines. [ABSTRACT FROM PUBLISHER]

In this paper we verify the impacts of the Joint Sparsity Model with Matrix Completion (JSM-MC) for the composition of training set in the context of face recognition using the Sparse Representation-based Classifier (SRC). A pre-processing step (histogram equalization) is performed in the face images to reduce the effects of illumination change. A clustering of training images is done to reduce the training set and uses the l1-norm of the sparse representation coefficients instead of the residuals for classification. The results are evaluated using a database with different illumina¬tion conditions and we also investigate the behavior of the system when the face image is partially occluded. [ABSTRACT FROM PUBLISHER]