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Showing papers on "Sparse approximation published in 2021"


Proceedings ArticleDOI
01 Jun 2021
TL;DR: Non-local sparse attention (NLSA) as mentioned in this paper is designed to retain long-range modeling capability from non-local operation while enjoying robustness and high-efficiency of sparse representation, which partitions the input space into hash buckets of related features.
Abstract: Both Non-Local (NL) operation and sparse representation are crucial for Single Image Super-Resolution (SISR). In this paper, we investigate their combinations and propose a novel Non-Local Sparse Attention (NLSA) with dynamic sparse attention pattern. NLSA is designed to retain long-range modeling capability from NL operation while enjoying robustness and high-efficiency of sparse representation. Specifically, NLSA rectifies non-local attention with spherical locality sensitive hashing (LSH) that partitions the input space into hash buckets of related features. For every query signal, NLSA assigns a bucket to it and only computes attention within the bucket. The resulting sparse attention prevents the model from attending to locations that are noisy and less-informative, while reducing the computational cost from quadratic to asymptotic linear with respect to the spatial size. Extensive experiments validate the effectiveness and efficiency of NLSA. With a few non-local sparse attention modules, our architecture, called non-local sparse network (NLSN), reaches state-of-the-art performance for SISR quantitatively and qualitatively.

216 citations


Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a new subspace clustering method to represent the data samples as linear combinations of the bases in a given dictionary, where the sparse structure is induced by low-rank factorization for the affinity matrix.
Abstract: Hyperspectral image super-resolution by fusing high-resolution multispectral image (HR-MSI) and low-resolution hyperspectral image (LR-HSI) aims at reconstructing high resolution spatial-spectral information of the scene. Existing methods mostly based on spectral unmixing and sparse representation are often developed from a low-level vision task perspective, they cannot sufficiently make use of the spatial and spectral priors available from higher-level analysis. To this issue, this paper proposes a novel HSI super-resolution method that fully considers the spatial/spectral subspace low-rank relationships between available HR-MSI/LR-HSI and latent HSI. Specifically, it relies on a new subspace clustering method named “structured sparse low-rank representation” (SSLRR), to represent the data samples as linear combinations of the bases in a given dictionary, where the sparse structure is induced by low-rank factorization for the affinity matrix. Then we exploit the proposed SSLRR model to learn the SSLRR along spatial/spectral domain from the MSI/HSI inputs. By using the learned spatial and spectral low-rank structures, we formulate the proposed HSI super-resolution model as a variational optimization problem, which can be readily solved by the ADMM algorithm. Compared with state-of-the-art hyperspectral super-resolution methods, the proposed method shows better performance on three benchmark datasets in terms of both visual and quantitative evaluation.

114 citations


Journal ArticleDOI
TL;DR: A combination of a mixture noise model with low-rank background may more accurately characterize complex distribution and outperforms the classic Reed-Xiaoli (RX), and the state-of-the-art detectors, such as robust principal component analysis (RPCA) with RX.
Abstract: Recently, the low-rank and sparse decomposition model (LSDM) has been used for anomaly detection in hyperspectral imagery. The traditional LSDM assumes that the sparse component where anomalies and noise reside can be modeled by a single distribution which often potentially confuses weak anomalies and noise. Actually, a single distribution cannot accurately describe different noise characteristics. In this article, a combination of a mixture noise model with low-rank background may more accurately characterize complex distribution. A modified LSDM, by modeling the sparse component as a mixture of Gaussian (MoG), is employed for hyperspectral anomaly detection. In the proposed framework, the variational Bayes (VB) algorithm is applied to infer a posterior MoG model. Once the noise model is determined, anomalies can be easily separated from the noise components. Furthermore, a simple but effective detector based on the Manhattan distance is incorporated for anomaly detection under complex distribution. The experimental results demonstrate that the proposed algorithm outperforms the classic Reed–Xiaoli (RX), and the state-of-the-art detectors, such as robust principal component analysis (RPCA) with RX.

106 citations


Journal ArticleDOI
TL;DR: A system architecture is developed, which contains UAVs integrated with monostatic multiple-input–multiple-output (MIMO) radars to estimate the direction-of-arrival (DOA) via MIMO radar and a novel sparse reconstruction algorithm is proposed.
Abstract: As an indispensable part of Internet of Vehicles (IoV), unmanned aerial vehicles (UAVs) can be deployed for target positioning and navigation in space-air-ground integrated networks (SAGIN) environment Maritime target positioning is very important for the safe navigation of ships, hydrographic surveys, and marine resource exploration Traditional methods typically exploit satellites to locate marine targets in SAGIN environment, and the location accuracy does not satisfy the requirements of modern ocean observation missions In order to localize marine target, we develop a system architecture in this paper, which contains UAVs integrated with monostatic multiple-input multiple-output (MIMO) radars The main thrust is to estimate direction-of-arrival (DOA) via MIMO radar Herein, we consider a general scenario that unknown mutual coupling exist, and a novel sparse reconstruction algorithm is proposed The mutual coupling matrix (MCM) is adopted with the help of its special structure, we formulate the data model as a sparse representation form Then two novel matrices, a weighted matrix and a reduced-dimensional matrix are constructed to reduce the computational complexity and enhance the sparsity, respectively Thereafter, a sparse constraint model is constructed using the concept of optimal weighted subspace fitting (WSF) Finally, DOA estimation of maritime targets can be achieved by reconstructing the support of a block sparse matrix Based on the DOA estimation results, multiple UAVs are used to cross-locate marine targets multiple times, and an accurate marine target position is achieved in SAGIN environment Numerical results are carried out, which demonstrates the effectiveness of the proposed DOA estimator, and the multi-UAV cooperative localization system can realize accurate target localization

73 citations



Journal ArticleDOI
TL;DR: A new semisupervised FE algorithm called a geodesic-based sparse manifold hypergraph (GSMH) that achieves satisfying FE performance with limited labeled training samples but also shows superiority compared with other state-of-the-art methods.
Abstract: Recently, the sparse representation (SR)-based graph embedding method has been extensively used in feature extraction (FE) tasks, but it is hard to reveal the complex manifold structure and multivariate relationship of samples in the hyperspectral image (HSI). Meanwhile, the small size sample problem in HSI data also limits the performance of the traditional SR approach. To tackle this problem, this article develops a new semisupervised FE algorithm called a geodesic-based sparse manifold hypergraph (GSMH). The presented method first utilizes the geodesic distance to measure the nonlinear similarity between samples lying on manifold space and further constructs the manifold neighborhood of each sample. Then, a geodesic-based neighborhood SR (GNSR) model is designed to explore the multivariate sparse correlations of different manifold neighborhoods. Considering the multivariate sparse manifold correlations among samples, a pair of semisupervised hypergraphs (HGs) is constructed to effectively incorporate the labeled and unlabeled training information in the embedding process and obtain the nonlinear discriminative feature representation for HSI. Experimental results on three HSI datasets indicate that the proposed method not only achieves satisfying FE performance with limited labeled training samples but also shows superiority compared with other state-of-the-art methods.

63 citations


Journal ArticleDOI
TL;DR: A novel multi-source fidelity sparse representation method is proposed, which can accurately realize multiple fault diagnosis of the gearbox without the prior knowledge regarding the number of fault sources.

48 citations


Journal ArticleDOI
TL;DR: CheXNet as mentioned in this paper uses convolutional support estimation network (CSEN) for classification of COVID-19 in X-ray images, achieving state-of-the-art performance in many classification tasks.
Abstract: Coronavirus disease (COVID-19) has been the main agenda of the whole world ever since it came into sight. X-ray imaging is a common and easily accessible tool that has great potential for COVID-19 diagnosis and prognosis. Deep learning techniques can generally provide state-of-the-art performance in many classification tasks when trained properly over large data sets. However, data scarcity can be a crucial obstacle when using them for COVID-19 detection. Alternative approaches such as representation-based classification [collaborative or sparse representation (SR)] might provide satisfactory performance with limited size data sets, but they generally fall short in performance or speed compared to the neural network (NN)-based methods. To address this deficiency, convolution support estimation network (CSEN) has recently been proposed as a bridge between representation-based and NN approaches by providing a noniterative real-time mapping from query sample to ideally SR coefficient support, which is critical information for class decision in representation-based techniques. The main premises of this study can be summarized as follows: 1) A benchmark X-ray data set, namely QaTa-Cov19, containing over 6200 X-ray images is created. The data set covering 462 X-ray images from COVID-19 patients along with three other classes; bacterial pneumonia, viral pneumonia, and normal. 2) The proposed CSEN-based classification scheme equipped with feature extraction from state-of-the-art deep NN solution for X-ray images, CheXNet, achieves over 98% sensitivity and over 95% specificity for COVID-19 recognition directly from raw X-ray images when the average performance of 5-fold cross validation over QaTa-Cov19 data set is calculated. 3) Having such an elegant COVID-19 assistive diagnosis performance, this study further provides evidence that COVID-19 induces a unique pattern in X-rays that can be discriminated with high accuracy.

47 citations


Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors proposed a local constraint-based sparse manifold hypergraph learning (LC-SMHL) algorithm to discover the manifold-based structure and the multivariate discriminant sparse relationship of hyperspectral image (HSI).
Abstract: Sparse representation-based graph embedding methods have been successfully applied to dimensionality reduction (DR) in recent years. However, these approaches usually become problematic in the presence of the hyperspectral image (HSI) that contains complex nonlinear manifold structure. Inspired by recent progress in manifold learning and hypergraph framework, a novel DR method named local constraint-based sparse manifold hypergraph learning (LC-SMHL) algorithm is proposed to discover the manifold-based sparse structure and the multivariate discriminant sparse relationship of HSI, simultaneously. The proposed method first designs a new sparse representation (SR) model named local constrained sparse manifold coding (LCSMC) by fusing local constraint and manifold reconstruction. Then, two manifold-based sparse hypergraphs are constructed with sparse coefficients and label information. Based on these hypergraphs, LC-SMHL learns an optimal projection for mapping data into low-dimensional space in which embedding features not only discover the manifold structure and sparse relationship of original data but also possess strong discriminant power for HSI classification. Experimental results on three real HSI data sets demonstrate that the proposed LC-SMHL method achieves better performance in comparison with some state-of-the-art DR methods.

46 citations


Journal ArticleDOI
TL;DR: A novel HS and MS image fusion method based on nonlocal low-rank tensor approximation and sparse representation, which shows the advantages of the proposed method over several state-of-the-art competitors.
Abstract: The fusion of hyperspectral (HS) and multispectral (MS) images designed to obtain high-resolution HS (HRHS) images is a very challenging work. A series of solutions has been proposed in recent years. However, the similarity in the structure of the HS image has not been fully used. In this article, we present a novel HS and MS image-fusion method based on nonlocal low-rank tensor approximation and sparse representation. Specifically, the HS image and the MS image are considered the spatially and spectrally degraded versions of the HRHS image, respectively. Then, the nonlocal low-rank constraint term is adopted in order to form the nonlocal similarity and the spatial–spectral correlation. Meanwhile, we add the sparse constraint term to describe the sparsity of abundance. Thus, the proposed fusion model is established and its optimization is solved by alternative direction method of multipliers (ADMM). The experimental results on three synthetic data sets and one real data set show the advantages of the proposed method over several state-of-the-art competitors.

44 citations


Journal ArticleDOI
TL;DR: In this paper, a multi-level sparse representation based identification (MSRI) algorithm is proposed for specific emitter identification (SEI) in the automatic identification system (AIS) pose a threat to maritime traffic safety management.
Abstract: Illegally forged signals in automatic identification system (AIS) pose a threat to maritime traffic safety management. In this paper, a multi-level sparse representation based identification (MSRI) algorithm is proposed for specific emitter identification (SEI) in the AIS. The MSRI innovatively combines neural networks with sparse representation based classification (SRC). Channel attention mechanism is introduced to a multi-scale convolutional neural network (CNN) for extracting hidden features in the signal. These extracted features are divided into shallow and deep features according to the depth of the network layer they are extracted from. The original AIS signals and the two-level features are spliced together to form a multi-level dictionary. Subsequently, a sparse representation based identification is performed on the decorrelated multi-level dictionary using the principal components analysis (PCA) method. The proposed MSRI is evaluated on a dataset composed of real-world AIS signals, and compared with the state-of-the-art identification algorithms. The evaluation is based on several factors including computational complexity, number of training samples, and number of emitters. Numerical results indicate that the proposed algorithm can identify emitters with higher accuracy and requires lower training time compared to other methods. Given more than 15 training samples at each emitter, the MSRI can identify nine emitters with an accuracy higher than 90%.

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed an image fusion method based on three-layer decomposition and sparse representation, where the source image is first decomposed into the high-frequency and low-frequency components, and the sparse reconstruct error parameter is adaptively designed according with the noise level.
Abstract: Image fusion has been received much attentions in recent years. However, solving both noise-free image fusion and noise-perturbed image fusion problems remains a big challenge. To solve the weak performance and low computational efficiency for current image fusion methods when dealing with the case of noisy source images, an image fusion method based on three-layer decomposition and sparse representation is proposed in this paper. In view of the high-pass characteristics of noise, the source image is first decomposed into the high-frequency and low-frequency components, and the sparse reconstruct error parameter is adaptively designed according with the noise level, so as to realize the fusion and denoising for high-frequency components simultaneously. To make full use of the details and energy in the low-frequency component, the structure–texture​ decomposition model is carried out and two fusion rules are carefully designed to fuse them. The fused image can be reconstructed by the perfused high-frequency, low-frequency structure and low-frequency texture layers. Experimental results demonstrate that the proposed method can effectively address the clean and noisy image fusion problems, and yield better performance than some state-of-the-art methods in terms of subjective visual and quantitative evaluations.

Journal ArticleDOI
TL;DR: The experimental results demonstrate that the proposed method can not only preserve the details and significant information of source images but also enhance the brightness of the fused image, when compared to other state-of-the-art fusion methods.
Abstract: The goal of infrared and visible image fusion is to generate an integrated image which can simultaneously preserve more visible detail information and prominent information from the input images. In order to achieve this goal, a novel infrared and visible fusion method based on visual saliency sparse representation and detail injection model (DIM) is presented. The proposed fusion method contains four steps. The first step is to decompose the source images into base layers and detail layers through the proposed multiscale decomposition method, which has the advantages of scale awareness and high-edge-preservation efficiency. Then, we design a novel subfusion rule called visual saliency sparse representation to get the fused base layer. Third, a DIM is developed to fuse the detail layers. In this model, we first employ a “max-absolute” scheme to obtain prefused images, which are then used as the model items for participating in the fusion process of the detail layers. Finally, the target-merged image is achieved through an inverse multiscale decomposition method. The experimental results demonstrate that the proposed method can not only preserve the details and significant information of source images but also enhance the brightness of the fused image, when compared to other state-of-the-art fusion methods.

Journal ArticleDOI
Yixiang Lu1, Zhenya Wang1, De Zhu1, Qingwei Gao1, Dong Sun1 
TL;DR: In this paper, a fault diagnosis algorithm based on clustering and sparse representation for rolling bearing is proposed, where the samples are first clustered by using their frequency spectrums instead of directly in time-domain waveforms and an adaptive redundant dictionary is trained over residuals between the original frequency spectrum and their component projected on the calculated cluster center.
Abstract: Bearing is one of the most important transmission components and breaks down more frequently than other parts in rotating equipment. The state detection of the bearing has a great significance for their working performance and safety in industrial production. In this article, we propose a very effective fault diagnosis algorithm based on clustering and sparse representation for rolling bearing. To obtain a robust cluster center for the acquired noisy samples, the samples are first clustered by using their frequency spectrums instead of directly in time-domain waveforms. Then, for each class of samples, an adaptive redundant dictionary is trained over residuals between the original frequency spectrums and their component projected on the calculated cluster center. The noise contained in a test sample is reduced by performing sparse coding and representation with the trained dictionary. Finally, the test sample belonging to a specific category is identified by selecting the maximum cosine similarity value between the reconstructed sample and cluster centers. Experimental results show that the proposed algorithm performs well on both simulated signals and real signals and exhibits advantages over other fault diagnosis methods in terms of diagnosis accuracy.

Journal ArticleDOI
TL;DR: The simulation and experimental results show that the self-adaptive dictionary with the atom extracted from the dual-channel TQWT has a stronger decomposition freedom and signal matching ability than orthogonal basis dictionaries, such as discrete cosine transform, discrete Hartley transform and discrete wavelet transform (DWT).

Journal ArticleDOI
Hong Peng1, Cancheng Li1, Jinlong Chao1, Tao Wang1, Chengjian Zhao1, Xiaoning Huo, Bin Hu1 
TL;DR: A novel sparse representation-based epileptic seizure classification based on the dictionary learning with homotopy (DLWH) algorithm is proposed and the results show that the epileptic detection system based onThe dictionary learning has a high application value.

Proceedings ArticleDOI
01 Jun 2021
TL;DR: SPARTA achieves new state-of-the-art results across a variety of open-domain question answering tasks in both English and Chinese datasets, including open SQuAD, CMRC and etc.
Abstract: We introduce SPARTA, a novel neural retrieval method that shows great promise in performance, generalization, and interpretability for open-domain question answering. Unlike many neural ranking methods that use dense vector nearest neighbor search, SPARTA learns a sparse representation that can be efficiently implemented as an Inverted Index. The resulting representation enables scalable neural retrieval that does not require expensive approximate vector search and leads to better performance than its dense counterpart. We validated our approaches on 4 open-domain question answering (OpenQA) tasks and 11 retrieval question answering (ReQA) tasks. SPARTA achieves new state-of-the-art results across a variety of open-domain question answering tasks in both English and Chinese datasets, including open SQuAD, CMRC and etc. Analysis also confirms that the proposed method creates human interpretable representation and allows flexible control over the trade-off between performance and efficiency.

Journal ArticleDOI
Yun Kong1, Zhaoye Qin1, Tianyang Wang1, Qinkai Han1, Fulei Chu1 
TL;DR: Comparative studies show that ESRIR outperforms the deep convolution neural network and four classical sparse representation-based classification methods on the recognition performances and computation costs.

Journal ArticleDOI
TL;DR: A linear spectral mixture model (LMM)-based end-to-end deep neural network named SNMF-Net is introduced for hyperspectral unmixing and its advantages over many state-of-the-art methods are shown.
Abstract: Hyperspectral unmixing is recognized as an important tool to learn the constituent materials and corresponding distribution in a scene. The physical spectral mixture model is always important to tackle this problem because of its highly ill-posed nature. In this article, we introduce a linear spectral mixture model (LMM)-based end-to-end deep neural network named SNMF-Net for hyperspectral unmixing. SNMF-Net shares an alternating architecture and benefits from both model-based methods and learning-based methods. On the one hand, SNMF-Net is of high physical interpretability as it is built by unrolling Lp sparsity constrained nonnegative matrix factorization (Lp-NMF) model belonging to LMM families. On the other hand, all the parameters and submodules of SNMF-Net can be seamlessly linked with the alternating optimization algorithm of Lp-NMF and unmixing problem. This enables us to reasonably integrate the prior knowledge on unmixing, the optimization algorithm, and the sparse representation theory into the network for robust learning, so as to improve unmixing. Experimental results on the synthetic and real-world data show the advantages of the proposed SNMF-Net over many state-of-the-art methods.

Journal ArticleDOI
TL;DR: The experimental results indicate that the LSDDPCRD performs better than eight classical and state-of-the-art AD algorithms on four real HSIs and could choose the fusing weights adaptively according to the characteristics of an HSI.
Abstract: The low-rank and sparse decomposition model (LSDM) has been widely studied by researchers and has successfully solved the problem of hyperspectral image (HSI) anomaly detection (AD) The traditional LSDM usually ignores the information of the low-rank matrix, which only detects the anomalous targets by using the sparse component To utilize both the sparse component and the low-rank component comprehensively, an anomaly detector for HSIs based on LSDM with density peak guided collaborative representation (LSDDPCRD) is proposed in this article First, the LSDM technique with the mixture of Gaussian model is used to decompose the original HSI, which can also alleviate the background noise contamination problem Then, the low-rank matrix is detected by the density peak guided collaborative representation detection algorithm, while the sparse matrix is calculated according to the Manhattan distance In addition, an entropy-based adaptive fusing method is designed to combine the results obtained from the low-rank matrix and the sparse component It could choose the fusing weights adaptively according to the characteristics of an HSI The experimental results indicate that the LSDDPCRD performs better than eight classical and state-of-the-art AD algorithms (GRX, LRX, SRX-Segmented, CRD, RPCA-RX, LSMAD, LRASR, and LSDM-MoG) on four real HSIs

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors proposed a two-path iterative framework for 3D SAR sparse imaging by mapping the AMP into a layer-fixed deep neural network, each layer of TPSSI-Net consists of four modules in cascade corresponding to four steps of the Onsager optimization.
Abstract: The emerging field of combining compressed sensing (CS) and three-dimensional synthetic aperture radar (3D SAR) imaging has shown significant potential to reduce sampling rate and improve image quality. However, the conventional CS-driven algorithms are always limited by huge computational costs and non-trivial tuning of parameters. In this article, to address this problem, we propose a two-path iterative framework dubbed TPSSI-Net for 3D SAR sparse imaging. By mapping the AMP into a layer-fixed deep neural network, each layer of TPSSI-Net consists of four modules in cascade corresponding to four steps of the AMP optimization. Differently, the Onsager terms in TPSSI-Net are modified to be differentiable and scaled by learnable coefficients. Rather than manually choosing a sparsifying basis, a two-path convolutional neural network (CNN) is developed and embedded in TPSSI-Net for nonlinear sparse representation in the complex-valued domain. All parameters are layer-varied and optimized by end-to-end training based on a channel-wise loss function, bounding both symmetry constraint and measurement fidelity. Finally, extensive SAR imaging experiments, including simulations and real-measured tests, demonstrate the effectiveness and high efficiency of the proposed TPSSI-Net.

Journal ArticleDOI
TL;DR: A new multi-View low rank sparse representation method based on three-way clustering to tackle challenges of dimensionality reduction and learning discriminative features from multi-view data and further proceed to get the relationship between the data items and clusters.
Abstract: During the past years, multi-view clustering algorithms have demonstrated satisfactory clustering results by fusing the multiple views of the dataset. Nowadays, the researches of dimensionality reduction and learning discriminative features from multi-view data have soared in the literatures. As for clustering, generating the suitable subspace of the high dimensional multi-view data is crucial to boost the clustering performance. In addition, the relationship between the original data and the clusters still remains uncovered. In this article, we design a new multi-view low rank sparse representation method based on three-way clustering to tackle these challenges, which derive the common consensus low dimensional representation from the multi-view data and further proceed to get the relationship between the data items and clusters. Specifically, we accomplish this goal by taking advantage of the low-rank and the sparse factor on the data representation matrix. The $$L_{2,1}$$ norm is imposed on error matrix to reduce the impact of noise contained in the data. Finally, a new objective function is constructed to preserve the consistency between the views by using the low-rank sparse representation technique. The weighted low-rank matrix is utilized to build the consensus low rank matrix. Then, the whole objective function is optimized by using the Augmented Lagrange’s Multiplier algorithm. Further, to find the uncertain relationship between the data items and the clusters, we pursue the neighborhood based three-way clustering technique to reflect the data items into core and fringe regions. Experiments conducted on the real-world datasets show the superior performance of the proposed method compared with the state-of-the-art algorithms.

Journal ArticleDOI
TL;DR: A novel sparse dictionary design method is proposed based on edited cepstrum to improve the precision of feature extraction, and the impulse response function is selected as sparse atom, which better reflects the structure and inherent modal characteristics of the faulty bearing.

Journal ArticleDOI
TL;DR: In this paper, a novel medical image fusion approach based on the segment graph filter (SGF) and sparse representation (SR) is proposed, where the edge information is integrated into the fused image as much as possible.

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors proposed a hybrid structural sparsification error (HSSE) model for image restoration, which jointly exploited image NSS prior using both the internal and external image data that provide complementary information.
Abstract: Recent works on structural sparse representation (SSR), which exploit image nonlocal self-similarity (NSS) prior by grouping similar patches for processing, have demonstrated promising performance in various image restoration applications. However, conventional SSR-based image restoration methods directly fit the dictionaries or transforms to the internal (corrupted) image data. The trained internal models inevitably suffer from overfitting to data corruption, thus generating the degraded restoration results. In this article, we propose a novel hybrid structural sparsification error (HSSE) model for image restoration, which jointly exploits image NSS prior using both the internal and external image data that provide complementary information. Furthermore, we propose a general image restoration scheme based on the HSSE model, and an alternating minimization algorithm for a range of image restoration applications, including image inpainting, image compressive sensing and image deblocking. Extensive experiments are conducted to demonstrate that the proposed HSSE-based scheme outperforms many popular or state-of-the-art image restoration methods in terms of both objective metrics and visual perception.

Journal ArticleDOI
Xia Xu1, Bin Pan1, Zongqing Chen1, Zhenwei Shi2, Tao Li1 
TL;DR: A new hyperspectral unmixing algorithm which integrates the idea of library pruning and sparse representation and can gradually compress the search space of sparse representation, which may relieve the loss of spectral information caused by the rapid compression of the library.
Abstract: Sparse hyperspectral unmixing has attracted increasing investigations during the past decade. Recent research has indicated that library pruning algorithms can significantly improve the unmixing accuracies by reducing the mutual coherence of the spectral library. Inspired by the good performance of library pruning, in this article we propose a new hyperspectral unmixing algorithm which integrates the idea of library pruning and sparse representation. An obvious challenge for pruning algorithms is that the real endmembers must be preserved after pruning. Unfortunately, recent proposed pruning algorithms, such as multiple signal classification are actually prepruning strategies, which cannot guarantee that the endmembers exactly exist in the selected spectral subset when the image noise is strong. To overcome this difficulty, we develop a simultaneous optimization approach which involves the pruning operation into the optimization process. Compared with existing prepruning-based unmixing methods, the proposed algorithm can gradually compress the search space of sparse representation, which may relieve the loss of spectral information caused by the rapid compression of the library. Instead of simply designing a regularizer, in this article we utilize a multiobjective-based framework where reconstruction error, sparsity error, and the pruning projection function are considered as three parallel objectives, so as to avoid the manually settings of regularization parameters. Moreover, we have provided theoretical analysis and proof for the reasonability of our pruning objective. Experiments on synthetic hyperspectral data may indicate the superiority of the proposed method under high-noise conditions.

Journal ArticleDOI
TL;DR: In this article, the Stein kernel-based sparse representation (SR) for EEG recordings was proposed for real-time seizure detection in the space of symmetric positive definite matrices, which form a Riemannian manifold.

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors proposed a sparse representation-based framework for remote sensing scene classification with deep feature fusion, where multilevel features are extracted from different layers of CNNs to fully exploit the feature learning ability.
Abstract: Scene classification of high-resolution remote sensing (RS) images has attracted increasing attentions due to its vital role in a wide range of applications. Convolutional neural networks (CNNs) have recently been applied on many computer vision tasks and have significantly boosted the performance including imagery scene classification, object detection, and so on. However, the classification performance heavily relies on the features that can accurately represent the scene of images, thus, how to fully explore the feature learning ability of CNNs is of crucial importance for scene classification. Another problem in CNNs is that it requires a large number of labeled samples, which is impractical in RS image processing. To address these problems, a novel sparse representation-based framework for small-sample-size RS scene classification with deep feature fusion is proposed. Specially, multilevel features are first extracted from different layers of CNNs to fully exploit the feature learning ability of CNNs. Note that the existing well-trained CNNs, e.g., AlexNet, VGGNet, and ResNet50, are used for feature extraction, in which no labeled samples is required. Then, sparse representation-based classification is designed to fuse the multilevel features, which is especially effective when only a small number of training samples are available. Experimental results over two benchmark datasets, e.g., UC-Merced and WHU-RS19, demonstrated that the proposed method can effectively fuse different levels of features learned in CNNs, and clearly outperform several state-of-the-art methods especially with limited training samples.

Journal ArticleDOI
TL;DR: In this paper, the authors mainly present an overview of the recent advances achieved in sparse representation-based medical image fusion, ranging from the conventional local and single-component SR-based methods to the latest global and multi-component sparse representationbased methods.
Abstract: Medical image fusion, which aims to combine multi-source information captured by different imaging modalities, is of great significance to medical professionals for precise diagnosis and treatment. In the last decade, sparse representation (SR)-based approach has emerged as a very active direction in the field of medical image fusion, due to its powerful ability for image representation. In this paper, we mainly present an overview of the recent advances achieved in SR-based medical image fusion, ranging from the conventional local and single-component SR-based methods to the latest global and multi-component SR-based methods. In addition, several major challenges remained in this direction are presented and some future prospects are discussed.

Journal ArticleDOI
TL;DR: This method makes it possible to accurately decompose vibration signal from faulty planetary subassemblies into two resonance components and noise, relying less on the setting of regularization parameters due to the noise restriction, and easier to detect potential fault information in decomposed low or high resonance component than in the original signal.