Joint Sparse Model Research Articles

Robust Interferometric ISAR Imaging With UAMP-Based Joint Sparse Signal Recovery

Sparse Bayesian learning (SBL) has found successful applications in interferometric inverse synthetic aperture radar (InISAR) imaging, especially in the presence of limited number of pulses or when using sparse apertures. SBL-based InISAR algorithms have been proven to be significantly superior to Fourier transform-based ones. However, the existing SBL-based algorithms are slow due to their high computational complexity. Moreover, there is also much room to improve in terms of imaging performance. In this work, leveraging the approximate message passing with unitary transformation (UAMP), we propose an InISAR imaging algorithm named UAMP-JSR (joint sparse recovery), which is much faster and delivers notably higher imaging accuracy than the existing SBL-based algorithms. Specifically, we develop a type-2 joint sparse model (JSM-2) for InISAR imaging and formulate it as a two-layer multiple measurement vectors (MMV) joint sparse problem. Based on a factor graph representation, the message passing techniques are used to efficiently solve this problem, which leads to the UAMP-JSR algorithm. Results based on extensive simulations and experiments based on the real data collected by the Pisa Radar (PIRAD) demonstrate the effectiveness and superiority of the proposed algorithm compared to existing algorithms.

Joint coupled dictionaries-based visible-infrared image fusion method via texture preservation structure in sparse domain

Joint sparse coding (JSC) and coupled dictionary learning (CDL) have been successful in visible and infrared image fusion tasks. However, JSC fusion methods using a single dictionary cannot perfectly characterize different modal signals. The CDL-based fusion methods ignore the role of high-frequency components in texture preservation and sharpness. In this study, we design fusion rules while considering the uniqueness and connection of different feature spaces to alleviate this issue. In the proposed fusion method, coupled dictionaries are first used to establish the relationship between infrared and visible images. Then, we use joint sparse coding and coupled dictionaries to calculate sparse coefficients via the joint sparse model. Finally, the improved coefficient fusion strategy realizes the conversion and synthesis between different spatial images. Experimental results on the TNO and RoadScene datasets show that the proposed method achieves promising results in terms of objective and subjective performance compared with state-of-the-art algorithms.

SAR Image Target Recognition Method by Global and Local Dictionary Sparse Representation

ABSTRACT Target variant detection has been challenging for Synthetic Aperture Radar (SAR), and the performance of the target variant recognition needs to be further enhanced. SAR images are widely used in the field of target recognition and are important to support reconnaissance operations. This paper proposes a novel research approach to improve the efficiency of target recognition. First, the theory surrounding sparse representation and dictionary learning is presented. Secondly, a SAR image target recognition model is constructed according to the theory. Meanwhile, a dynamic joint sparse representation model is proposed based on multi-information and applied to SAR image target recognition when sparse representation is under consideration. Finally, experiments are set up to validate the proposed model. The results are presented as follows: 1. the recognition rate range of the sample SAR image target is 0.811–0.995 and 0.867–0.990, respectively, when the two cases, without registration processing and with registration processing, utilize the recognition method of dictionary learning and sparse representation. 2. with the increase in dictionary size, the average recognition rate of SAR images based on multi-information dynamic joint sparse representation also increases under the conditions of no logarithm transformation and median filtering and after logarithm transformation and median filtering are run. Then, the average recognition rate range is 0.63–0.9 and 0.65–0.96, respectively. Thus, the recognition rate is improved by 5%-10%. 3. the recognition approach based on the sparse representation of multi-information dynamic joint has distinct sparse degrees of SAR image recognition in the case of logarithmic transformation and median filtering when the sample image has been registered and not been registered. The relative average recognition rates were 0.950–0.970 and 0.955–0.977, respectively. The key contribution of the research is to offer a workable solution to the issues plaguing SAR target variant recognition and to improve the significant limitations in the state-of-the-art literature thoroughly.

Distributed Multi-View Sparse Vector Recovery

In this paper, we consider a multi-view compressed sensing problem, where each sensor can only obtain a partial view of the global sparse vector. Here the partial view means that some arbitrary and unknown indices of the global vector are unobservable to that sensor and do not contribute to the measurement outputs. The sensors aim to collaboratively recover the global state vector in a decentralized manner. We formulate this recovery problem as a bilinear optimization problem relying on a factored joint sparsity model (FJSM), in which the variables are factorized into a node-specific sparse local masking vector and the desired common sparse global vector. We first theoretically analyze the general conditions guaranteeing the global vector's successful recovery. Then we propose a novel in-network algorithm based on the powerful distributed alternating direction method of multipliers (ADMM), which can reconstruct the vectors and achieve consensus among nodes concerning the estimation of the global vector. Specifically, each node alternately updates the common global vector and its local masking vector, and then it transfers the estimated global vector to its neighboring nodes for further updates. To avoid potential divergence of the iterative algorithm, we propose an early stopping rule for the estimation of the local masking vectors and further conceive an estimation error-mitigation algorithm. The convergence of the proposed algorithms is theoretically proved. Finally, extensive simulations validate their excellent performance both in terms of the convergence and recovery accuracy.

Application of matrix multiplication in signal sensor image perception

Abstract LOT wireless sensor nodes are limited by physical factors, usually have weak computing power and endurance, and wireless communication methods are very vulnerable to information theft. Therefore, it is of great significance to ensure the safe and efficient transmission of images in new application scenarios. In view of the need for an efficient image transmission, this paper combines compressed sensing technology with p-tensor product theory, applies the above theory to distributed wireless sensor networks, and uses the correlation of adjacent sensor nodes in wireless sensor networks to propose an improved a joint sparse model for measurement matrices and reduction algorithms. The feasibility is verified by simulation experiments, and the comparison between joint reconstruction and single reconstruction, and the application of various algorithms in other algorithms is carried out, and the actual completion time and storage capacity are analysed. The minimum completion time for wavelet transform is 1.29, the sparse estimated time for the selection of preliminary P waves is 0.07 and the compressed sensing time is 0.20. The maximum completion time for wavelet transform was 1.32, for sparse estimation, it is 0.62, for preliminary P-wave selection, it is 0.17, and for compressed sensing, it is 0.88. The processing time is no >3 s and the runtime is only 0.22–0.88 s. The results show that compared with the compressed sensing of a single node, the joint sparse model based on distributed compressed sensing has a smaller reconstruction error, and can achieve high-precision signal reconstruction when the measurement value is small.

Composite Kernel of Mutual Learning on Mid-Level Features for Hyperspectral Image Classification.

By training different models and averaging their predictions, the performance of the machine-learning algorithm can be improved. The performance optimization of multiple models is supposed to generalize further data well. This requires the knowledge transfer of generalization information between models. In this article, a multiple kernel mutual learning method based on transfer learning of combined mid-level features is proposed for hyperspectral classification. Three-layer homogenous superpixels are computed on the image formed by PCA, which is used for computing mid-level features. The three mid-level features include: 1) the sparse reconstructed feature; 2) combined mean feature; and 3) uniqueness. The sparse reconstruction feature is obtained by a joint sparse representation model under the constraint of three-scale superpixels' boundaries and regions. The combined mean features are computed with average values of spectra in multilayer superpixels, and the uniqueness is obtained by the superposed manifold ranking values of multilayer superpixels. Next, three kernels of samples in different feature spaces are computed for mutual learning by minimizing the divergence. Then, a combined kernel is constructed to optimize the sample distance measurement and applied by employing SVM training to build classifiers. Experiments are performed on real hyperspectral datasets, and the corresponding results demonstrated that the proposed method can perform significantly better than several state-of-the-art competitive algorithms based on MKL and deep learning.

Machine learning-enabled MIMO-FBMC communication channel parameter estimation in IIoT: A distributed CS approach

Compressed Sensing (CS) is a Machine Learning (ML) method, which can be regarded as a single-layer unsupervised learning method. It mainly emphasizes the sparsity of the model. In this paper, we study an ML-based CS Channel Estimation (CE) method for wireless communications, which plays an important role in Industrial Internet of Things (IIoT) applications. For the sparse correlation between channels in Multiple Input Multiple Output Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation (MIMO-FBMC/OQAM) systems, a Distributed Compressed Sensing (DCS)-based CE approach is studied. A distributed sparse adaptive weak selection threshold method is proposed for CE. Firstly, the correlation between MIMO channels is utilized to represent a joint sparse model, and CE is transformed into a joint sparse signal reconstruction problem. Then, the number of correlation atoms for inner product operation is optimized by weak selection threshold, and sparse signal reconstruction is realized by sparse adaptation. The experiment results show that the proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher CE performance than classical Orthogonal Matching Pursuit (OMP) method and other traditional DCS methods in the time-frequency dual selective channels.

A Face Recognition Method Based on Multifeature Fusion

Face recognition is widely used in daily life and has an important supporting role for social management. Face recognition is mainly based on historical accumulation data to confirm people’s identities in unknown samples and obtain valuable intelligence information. For the problem of face recognition, this paper proposes a multifeature joint adaptive weighting algorithm framework. In this method, a number of different types of features are first used to describe the face characteristics. The selected features should be as complementary as possible, and the overlap redundant information should be reduced to the greatest extent, so as to ensure the performance and efficiency of multifeature fusion. In the classification stage, based on the joint sparse representation model, the multiple types of features are characterized, and their reconstruction error vectors for the corresponding features of the test sample are calculated. The joint sparse representation model can examine the correlation between different types of features, thereby improving the accuracy of representation and fully integrating the advantages of multiple types of features. At the same time, in view of the simple superposition of reconstruction errors in the traditional sparse representation model, this paper uses a random weight matrix to comprehensively consider the weighted reconstruction errors under different weight conditions, so as to obtain statistical decision quantities for the final decision. The framework proposed in this paper can adapt to different multifeature combinations and has good practicability. In the experiment, training and test sets are constructed based on public face image data sets to test the proposed method. The experimental results show that the method in this paper is more effective and robust compared with some present methods for face recognition.

Joint sparse model with coupled dictionary for medical image fusion

Medical image fusion has made great progress benefit from strong feature association ability of joint sparse model. Since medical images are obtained by different sensors with different imaging mechanisms, single dictionary cannot effectively characterize innovation and common information, which becomes stumbling block for effective fusion. In this paper, we propose novel joint sparse model with coupled dictionary learning for medical image fusion. Our framework design new fusion rule to enhance ability of multi-source signal preservation and retain edge/texture information. Firstly, source medical images was represented as a common sparse component and innovation sparse components with over-complete coupled dictionaries. Secondly, fused sparse coefficient is obtained by using the designed novel rule. Finally, fused results are reconstructed using fused coefficients and coupled dictionaries. Our proposed method is evaluated and compared with 9 state-of-the-art fusion methods on public Harvard dataset. Qualitative and quantitative results show that our method is generally better than the compared methods in terms of retaining source image structural and functional information.

A novel joint sparse regularization model to structural damage identification by the generalized fused lasso penalty

The sparse regularization (SReg) model is widely used for structural damage identification by employing the sparsity peculiarity of the structural damage. The conventional SReg model often separately conducts damage identification on each measurement, while ignores the utilization of the similarity information among different measurements. In this paper, we propose a novel joint sparse regularization model for structural damage identification. In detail, combined with the sparsity of the structural damage by the conventional SReg model, our model employs the similarity of different measurements to further improve the damage identification performance, which is realized by the generalized fused lasso penalty. Numerical simulations on the six-bay planar truss structure and experimental studies on the cantilever beam structure illustrate that the damage identification accuracy of our model is promoted 3.59% and 20.75% than the conventional SReg one, respectively.

A Hybrid Sparse Representation Model for Image Restoration.

Group-based sparse representation (GSR) uses image nonlocal self-similarity (NSS) prior to grouping similar image patches, and then performs sparse representation. However, the traditional GSR model restores the image by training degraded images, which leads to the inevitable over-fitting of the data in the training model, resulting in poor image restoration results. In this paper, we propose a new hybrid sparse representation model (HSR) for image restoration. The proposed HSR model is improved in two aspects. On the one hand, the proposed HSR model exploits the NSS priors of both degraded images and external image datasets, making the model complementary in feature space and the plane. On the other hand, we introduce a joint sparse representation model to make better use of local sparsity and NSS characteristics of the images. This joint model integrates the patch-based sparse representation (PSR) model and GSR model, while retaining the advantages of the GSR model and the PSR model, so that the sparse representation model is unified. Extensive experimental results show that the proposed hybrid model outperforms several existing image recovery algorithms in both objective and subjective evaluations.

The Multi-domain Union Clutter Suppression Algorithm Based on Robust Principal Component Analysis

In through-the-wall imaging, the clutter cannot be eliminated completely through traditional algorithms, and seriously affects the subsequent target detection and recognition. To solve the problem, based on robust principal component analysis theory, a joint low-rank and sparse model is established in echo and image domain respectively. The models are solved by smoothing fast alternating linearization method. Then, the target images are dealt with exponentially weighted multiply multi-domain image fusion to obtain the final image. The simulation results indicate that the algorithm has great speed and accuracy with effective improvement on imaging quality of targets.

Combination of Joint Representation and Adaptive Weighting for Multiple Features with Application to SAR Target Recognition

For the synthetic aperture radar (SAR) target recognition problem, a method combining multifeature joint classification and adaptive weighting is proposed with innovations in fusion strategies. Zernike moments, nonnegative matrix factorization (NMF), and monogenic signal are employed as the feature extraction algorithms to describe the characteristics of original SAR images with three corresponding feature vectors. Based on the joint sparse representation model, the three types of features are jointly represented. For the reconstruction error vectors from different features, an adaptive weighting algorithm is used for decision fusion. That is, the weights are adaptively obtained under the framework of linear fusion to achieve a good fusion result. Finally, the target label is determined according to the fused error vector. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset under the standard operating condition (SOC) and four extended operating conditions (EOC), i.e., configuration variants, depression angle variances, noise interference, and partial occlusion. The results verify the effectiveness and robustness of the proposed method.

Model-based decentralized Bayesian algorithm for distributed compressed sensing

In this paper, a novel model-based distributed compressive sensing (DCS) algorithm is proposed. DCS exploits the inter-signal correlations and has the capability to jointly recover multiple sparse signals. Proposed approach is a Bayesian decentralized algorithm which uses the type 1 joint sparsity model (JSM-1) and exploits the intra-signal correlations, as well as the inter-signal correlations. Compared to the conventional DCS algorithm, which only exploit the joint sparsity of the signals, the proposed approach takes the intra- and inter-scale dependencies among the wavelet coefficients into account to enable the utilization of the individual signal structure. Furthermore, the Bessel K-form (BKF) is used as the prior distribution which has a sharper peak at zero and heavier tails than the Gaussian distribution. The variational Bayesian (VB) inference is employed to perform the posterior distributions and acquire a closed-form solution for model parameters. Simulation results demonstrate that the proposed algorithm have good recovery performance in comparison with state-of the-art techniques.

Stable Anatomy Detection in Multimodal Imaging Through Sparse Group Regularization: A Comparative Study of Iron Accumulation in the Aging Brain.

Multimodal neuroimaging provides a rich source of data for identifying brain regions associated with disease progression and aging. However, present studies still typically analyze modalities separately or aggregate voxel-wise measurements and analyses to the structural level, thus reducing statistical power. As a central example, previous works have used two quantitative MRI parameters—R2* and quantitative susceptibility (QS)—to study changes in iron associated with aging in healthy and multiple sclerosis subjects, but failed to simultaneously account for both. In this article, we propose a unified framework that combines information from multiple imaging modalities and regularizes estimates for increased interpretability, generalizability, and stability. Our work focuses on joint region detection problems where overlap between effect supports across modalities is encouraged but not strictly enforced. To achieve this, we combine L1 (lasso), total variation (TV), and L2 group lasso penalties. While the TV penalty encourages geometric regularization by controlling estimate variability and support boundary geometry, the group lasso penalty accounts for similarities in the support between imaging modalities. We address the computational difficulty in this regularization scheme with an alternating direction method of multipliers (ADMM) optimizer. In a neuroimaging application, we compare our method against independent sparse and joint sparse models using a dataset of R2* and QS maps derived from MRI scans of 113 healthy controls: our method produces clinically-interpretable regions where specific iron changes are associated with healthy aging. Together with results across multiple simulation studies, we conclude that our approach identifies regions that are more strongly associated with the variable of interest (e.g., age), more accurate, and more stable with respect to training data variability. This work makes progress toward a stable and interpretable multimodal imaging analysis framework for studying disease-related changes in brain structure and can be extended for classification and disease prediction tasks.

Safety Monitoring and Evaluation of Construction Projects Based on Multi-sensor Fusion

For most construction projects, the complex engineering environment, the backward data collection technology, and the unreasonable monitoring network have resulted in many problems in monitoring data such as lots of noise and missing data items, therefore, it is of great significance to study the safety monitoring system of construction projects based on wireless sensor network (WSN). For this reason, this paper proposed a construction safety monitoring and evaluation (CSME) model based on multi-sensor fusion. First, the system structure and data flow model of the construction safety monitoring system were constructed; then, combining with a multi-sensor deep fusion system which was built on physical and information systems, this paper designed a spectrum sensing algorithm for sensor signals within the construction area. After that, tempo-spatial correlation analysis was conducted on the monitoring data, and a multi-sensor monitoring network joint sparse (MSMN-JS) model was constructed, which realized reconstruction of missing data items. At last, this paper used experimental results to prove the application value of the algorithm model to the safety monitoring and evaluation of construction projects.

Sparse aperture bistatic ISAR imaging under low signal-to-noise ratio condition

A sparse representation-based bistatic inverse synthetic aperture radar (ISAR) imaging method can achieve a high-resolution image of a target with sparse aperture data. However, the bistatic ISAR system is more sensitive to noise than the monostatic one because of its nonmirror reflection geometry. To overcome this drawback, we propose the sparse aperture bistatic ISAR imaging method based on joint sparse model. Considering the joint sparse information of bistatic ISAR echo, a joint sparse imaging model is constructed. Then, the dechirped sparse aperture bistatic ISAR echo after translational compensation is transformed into range fast time and azimuth slow time domains by the joint sparse imaging model, and a corresponding azimuth sparse basis is constructed. Then a joint sparse complex approximate message passing algorithm is proposed to joint sparse imaging model. The joint sparse imaging problem is converted to a block sparse imaging problem by vectorization. Using the relationship between the vectorization of the matrix and the Kronecker product, a matrix iteration structure is proposed to solve the joint sparse model efficiently and accurately. The experimental results based on both scattering point model and electromagnetic calculation model data verify the effectiveness of the proposed imaging method.

Multi-channel Joint Sparse Learning Model for Non-rigid Three-dimensional Object Classification

Abstract In order to solve the issues of inadequate feature description and inefficient feature learning model existing in current classification methods, this article proposes a multi-channel joint sparse learning model for three-dimensional (3D) non-rigid object classification. First, the authors adopt a multi-level measurement of intrinsic properties to create complementary shape descriptors. Second, they build independent and informative bag of features (BoF) by embedding these shape descriptors into the visual vocabulary space. Third, a max-dependency and min-redundancy criterion is applied for optimal feature filtering on each BoF dictionary based on mutual information; meanwhile, each dictionary is learned and weighted according to its contribution to the classification task, and then a compact multi-channel joint sparse learning model is constructed. Finally, the authors train the joint sparse learning model followed by a Softmax classifier to implement efficient shape classification. The experimental results show that the proposed method has stronger feature representation ability and promotes greatly the discrimination of sparse coding coefficients. Thus, the promising classification performance and the powerful robustness can be obtained compared to the state-of-the-art methods.

Sparse Bayesian Perspective for Radar Coincidence Imaging with Model Errors

Sparsity-driven methods are commonly applied to reconstruct targets in radar coincidence imaging (RCI), where the reference matrix needs to be computed precisely and the prior knowledge of the accurate imaging model is essential. Unfortunately, the existence of model errors in practical RCI applications is common, which defocuses the reconstructed image considerably. Accordingly, this paper aims to formulate a unified framework for sparsity-driven RCI with model errors based on the sparse Bayesian approach. Firstly, a parametric joint sparse reconstruction model is built to describe the RCI when perturbed by model errors. The structured sparse Bayesian prior is then assigned to this model, after which the structured sparse Bayesian autofocus (SSBA) algorithm is proposed in the variational Bayesian expectation maximization (VBEM) framework; this solution jointly realizes sparse imaging and model error calibration. Simulation results demonstrate that the proposed algorithm can both calibrate the model errors and obtain a well-focused target image with high reconstruction accuracy.

Temporal frequency joint sparse optimization and fuzzy fusion for motor imagery-based brain-computer interfaces

BackgroundMotor imagery (MI) related features are typically extracted from a fixed frequency band and time window of EEG signal. Meanwhile, the time when the brain activity associated with the occurring task varies from person to person and trial to trial. Thus, some of the discarded EEG data with time may contain MI-related information. New methodThis study proposes a temporal frequency joint sparse optimization and fuzzy fusion (TFSOFF) method for joint frequency band optimization and classification fusion on multiple time windows to effectively utilize the signals of all time period within the MI task. Raw EEG data are first segmented into multiple subtime windows using a sliding window approach. Then, a set of overlapping bandpass filters is performed on each time window to generate a set of overlapping subbands, and common spatial pattern is used for feature extraction at each subband. Joint frequency band optimization is conducted on multiple time windows using a joint sparse optimization model. Fuzzy integral is used to fuse each time window after joint optimization. ResultsThe proposed TFSOFF is validated on two public EEG datasets and compared with several other competing methods. Experimental results show that the proposed TFSOFF can effectively extract MI related features of all time period EEG signals within the MI task and helps improving the classification performance of MI. Comparison with existing methodsThe proposed TFSOFF exhibits superior performance in comparison with several competing methods. ConclusionsThe proposed method is a suitable method for improving the performance of MI-based BCIs.