Direct data domain STAP using sparse representation of clutter spectrum

In the field of space-time adaptive processing (STAP), direct data domain (D3) methods avoid non-stationary training data and can effectively suppress the clutter within the test cell. However, this benefit comes at the cost of a reduced system degree of freedom (DOF), which results in performance loss. In this paper, by exploiting the intrinsic sparsity of the spectral distribution, a new direct data domain approach using sparse representation (D3SR) is proposed, which seeks to estimate the high-resolution space-time spectrum only with the test cell. The simulation of both side-looking and non-side-looking cases has illustrated the effectiveness of the D3SR spectrum estimation using focal underdetermined system solution (FOCUSS) and L₁ norm minimization. Then the clutter covariance matrix (CCM) and the corresponding adaptive filter can be effectively obtained. D3SR maintains the full system DOF so that it can achieve better performance of output signal-clutter-ratio (SCR) and minimum detectable velocity (MDV) than current D3 methods, e.g., direct data domain least squares (D3LS). Therefore D3SR can deal with the non-stationary clutter scenario more effectively, where both the discrete interference and range-dependent clutter exists.     

An algorithm for dictionary generation in sparse representation

The K-COD (K-Complete Orthogonal Decomposition) algorithm for generating adaptive dic-tionary for signals sparse representation in the framework of K-means clustering is proposed in this paper, in which rank one approximation for components assembling signals based on COD and K-means clustering based on chaotic random search are well utilized. The results of synthetic test and empirical experiment for the real data show that the proposed algorithm outperforms recently reported alternatives: K-Singular Value Decomposition (K-SVD) algorithm and Method of Optimal Directions (MOD) algorithm.     

Image super-resolution reconstruction based on sparse representation and deep learning

Super-resolution reconstruction technology has important scientific significance and application value in the field of image processing by performing image restoration processing on one or more low-resolution images to improve image spatial resolution. Based on the SCSR algorithm and VDSR network, in order to further improve the image reconstruction quality, an image super-resolution reconstruction algorithm combined with multi-residual network and multi-feature SCSR(MRMFSCSR) is proposed. Firstly, at the sparse reconstruction stage, according to the characteristics of image blocks, our algorithm extracts the contour features of non-flat blocks by NSCT transform, extracts the texture features of flat blocks by Gabor transform, then obtains the reconstructed high-resolution (HR) images by using sparse models. Secondly, according to improve the VDSR deep network and introduce the feature fusion idea, the multi-residual network structure (MR) is designed. The reconstructed HR image obtained by the sparse reconstruction stage is used as the input of the MR network structure to optimize the high-frequency detail residual information. Finally, we can obtain a higher quality super-resolution image compared with the SCSR algorithm and the VDSR algorithm.     

Adjusting samples for obtaining better l2-norm minimization based sparse representation

l₂-norm sparse representation (l₂-SR) based face recognition method has attracted increasing attention due to its excellent performance, simple algorithm and high computational efficiency. However, one of the drawbacks of l₂-SR is that the test sample may be conspicuous difference from the training samples even from the same class and thus the method shows poor robustness. Another drawback is that l₂-SR does not perform well in identifying the training samples that are trivial in correctly classifying the test sample. In this paper, to avoid the above imperfection, we proposed a novel l₂-SR. We first identifies the training samples that are important in correctly classifying the test sample and then neglects components that cannot be represented by the training samples. The proposed method also involve in-depth analysis of l₂-SR and provide novel ideas to improve previous methods. Experimental results on face datasets show that the proposed method can greatly improve l₂-SR.     

Pose-based human action recognition via sparse representation in dissimilarity space

Human actions can be considered as a sequence of body poses over time, usually represented by coordinates corresponding to human skeleton models. Recently, a variety of low-cost devices have been released, able to produce markerless real time pose estimation. Nevertheless, limitations of the incorporated RGB-D sensors can produce inaccuracies, necessitating the utilization of alternative representation and classification schemes in order to boost performance. In this context, we propose a method for action recognition where skeletal data are initially processed in order to obtain robust and invariant pose representations and then vectors of dissimilarities to a set of prototype actions are computed. The task of recognition is performed in the dissimilarity space using sparse representation. A new publicly available dataset is introduced in this paper, created for evaluation purposes. The proposed method was also evaluated on other public datasets, and the results are compared to those of similar methods.     

Collaborative object tracking model with local sparse representation

There existed many visual tracking methods that are based on sparse representation model, most of them were either generative or discriminative, which made object tracking more difficult when objects have undergone large pose change, illumination variation or partial occlusion. To address this issue, in this paper we propose a collaborative object tracking model with local sparse representation. The key idea of our method is to develop a local sparse representation-based discriminative model (SRDM) and a local sparse representation-based generative model (SRGM). In the SRDM module, the appearance of a target is modeled by local sparse codes that can be formed as training data for a linear classifier to discriminate the target from the background. In the SRGM module, the appearance of the target is represented by sparse coding histogram and a sparse coding-based similarity measure is applied to compute the distance between histograms of a target candidate and the target template. Finally, a collaborative similarity measure is proposed for measuring the difference of the two models, and then the corresponding likelihood of the target candidates is input into a particle filter framework to estimate the target state sequentially over time in visual tracking. Experiments on some publicly available benchmarks of video sequences showed that our proposed tracker is robust and effective.     

Locality-sensitive kernel sparse representation classification for face recognition

In this paper a new classification method called locality-sensitive kernel sparse representation classification (LS-KSRC) is proposed for face recognition. LS-KSRC integrates both sparsity and data locality in the kernel feature space rather than in the original feature space. LS-KSRC can learn more discriminating sparse representation coefficients for face recognition. The closed form solution of the l₁-norm minimization problem for LS-KSRC is also presented. LS-KSRC is compared with kernel sparse representation classification (KSRC), sparse representation classification (SRC), locality-constrained linear coding (LLC), support vector machines (SVM), the nearest neighbor (NN), and the nearest subspace (NS). Experimental results on three benchmarking face databases, i.e., the ORL database, the Extended Yale B database, and the CMU PIE database, demonstrate the promising performance of the proposed method for face recognition, outperforming the other used methods.     

Color image quality assessment based on sparse representation and reconstruction residual

Image quality assessment (IQA) is a fundamental problem in image processing. While in practice almost all images are represented in the color format, most of the current IQA metrics are designed in gray-scale domain. Color influences the perception of image quality, especially in the case where images are subject to color distortions. With this consideration, this paper presents a novel color image quality index based on Sparse Representation and Reconstruction Residual (SRRR). An overcomplete color dictionary is first trained using natural color images. Then both reference and distorted images are represented using the color dictionary, based on which two feature maps are constructed to measure structure and color distortions in a holistic manner. With the consideration that the feature maps are insensitive to image contrast change, the reconstruction residuals are computed and used as a complementary feature. Additionally, luminance similarity is also incorporated to produce the overall quality score for color images. Experiments on public databases demonstrate that the proposed method achieves promising performance in evaluating traditional distortions, and it outperforms the existing metrics when used for quality evaluation of color-distorted images.     

Modeling and recognizing action contexts in persons using sparse representation

This paper proposes a novel dynamic sparsity-based classification scheme to analyze various interaction actions between persons. To address the occlusion problem, this paper represents an action in an over-complete dictionary to makes errors (caused by lighting changes or occlusions) sparsely appear in the training library if the error cases are well collected. Because of this sparsity, it is robust to occlusions and lighting changes. In addition, a novel Hamming distance classification (HDC) scheme is proposed to classify action events to various types. Because the nature of Hamming code is highly tolerant to noise, the HDC scheme is also robust to environmental changes. The difficulty of complicated action modeling can be easily tackled by adding more examples to the over-complete dictionary. More importantly, the HDC scheme is very efficient and suitable for real-time applications because no minimization process is involved to calculate the reconstruction error.     

A video semantic detection method based on locality-sensitive discriminant sparse representation and weighted KNN

Video semantic detection has been one research hotspot in the field of human-computer interaction. In video features-oriented sparse representation, the features from the same category video could not achieve similar coding results. To address this, the Locality-Sensitive Discriminant Sparse Representation (LSDSR) is developed, in order that the video samples belonging to the same video category are encoded as similar sparse codes which make them have better category discrimination. In the LSDSR, a discriminative loss function based on sparse coefficients is imposed on the locality-sensitive sparse representation, which makes the optimized dictionary for sparse representation be discriminative. The LSDSR for video features enhances the power of semantic discrimination to optimize the dictionary and build the better discriminant sparse model. More so, to further improve the accuracy of video semantic detection after sparse representation, a weighted K-Nearest Neighbor (KNN) classification method with the loss function that integrates reconstruction error and discrimination for the sparse representation is adopted to detect video semantic concepts. The proposed methods are evaluated on the related video databases in comparison with existing sparse representation methods. The experimental results show that the proposed methods significantly enhance the power of discrimination of video features, and consequently improve the accuracy of video semantic concept detection.     

Near-field time-frequency localization method using sparse representation

This paper presents a novel near-field source localization method based on the time-frequency sparse model.Firstly,the method converts the time domain data of array output into time-frequency domain by time-frequency transform;then constructs sparse localization model by utilizing the specially selected time-frequency points,and finally the greedy algorithms are chosen to solve the sparse problem to localize the source.When the coherent sources exist,we propose an additional iterative selection procedure to improve the estimation performance.The proposed method is suitable for uncorrelated and coherent sources,moreover,the improved estimation accuracy and the robustness to low signal to noise ratio(SNR) are achieved.Simulations results verify the efficiency of the proposed algorithm.     

Object tracking using discriminative sparse appearance model

Object tracking based on sparse representation formulates tracking as searching the candidate with minimal reconstruction error in target template subspace. The key problem lies in modeling the target robustly to vary appearances. The appearance model in most sparsity-based trackers has two main problems. The first is that global structural information and local features are insufficiently combined because the appearance is modeled separately by holistic and local sparse representations. The second problem is that the discriminative information between the target and the background is not fully utilized because the background is rarely considered in modeling. In this study, we develop a robust visual tracking algorithm by modeling the target as a model for discriminative sparse appearance. A discriminative dictionary is trained from the local target patches and the background. The patches display the local features while their position distribution implies the global structure of the target. Thus, the learned dictionary can fully represent the target. The incorporation of the background into dictionary learning also enhances its discriminative capability. Upon modeling the target as a sparse coding histogram based on this learned dictionary, our tracker is embedded into a Bayesian state inference framework to locate a target. We also present a model update scheme in which the update rate is adjusted automatically. In conjunction with the update strategy, the proposed tracker can handle occlusion and alleviate drifting. Comparative results on challenging benchmark image sequences show that the tracking method performs favorably against several state-of-the-art algorithms.     

Wideband source localization using sparse learning via iterative minimization

In this paper, two extensions of the Sparse Learning via Iterative Minimization (SLIM) algorithm are presented for wideband source localization using a sensor array. The proposed methods exploit the joint sparse structure across all frequency bins, and estimate the spatial pseudo-spectra at various frequency bins jointly and iteratively. Via several numerical examples, we show that the proposed methods can provide high-resolution angle estimates and excellent source localization performance, and are able to resolve the left–right ambiguity problem, when used together with the vector sensor array technology.     

Passive localization of mixed sources jointly using MUSIC and sparse signal reconstruction

Source localization for mixed far-field and near-field sources is considered. By constructing the second-order statistics domain data of array which is only related to DOA parameters of mixed sources, we obtain the DOA estimation of all sources using the weighted ℓ₁-norm minimization. And then, we use MUSIC spectral function to distinguish the mixed sources as well as to provide a more accurate DOA estimation of far-field sources. Finally, a mixed overcomplete matrix on the basis of DOA estimation is introduced in the sparse signal representation framework to estimate range parameters. The performance of the proposed method is verified by numerical simulations and is also compared with two existing methods.     

L0-norm sparse representation based on modified genetic algorithm for face recognition

The typical sparse representation for classification (SRC) exploits the training samples to represent the test samples, and classifies the test samples based on the representation results. SRC is essentially an L₀-norm minimization problem which can theoretically yield the sparsest representation and lead to the promising classification performance. We know that it is difficult to directly resolve L₀-norm minimization problem by applying usual optimization method. To effectively address this problem, we propose the L₀-norm based SRC by exploiting a modified genetic algorithm (GA), termed GASRC, in this paper. The basic idea of GASRC is that it modifies the traditional genetic algorithm and then uses the modified GA (MGA) to select a part of the training samples to represent a test sample. Compared with the conventional SRC based on L₁-norm optimization, GASRC can achieve better classification performance. Experiments on several popular real-world databases show the good classification effectiveness of our approach.     

基于稀疏表示的双平行线阵二维DOA估计

为了对空域目标的方位角和俯仰角进行有效估计,提出一种基于稀疏表示的双平行线阵二维DOA估计方法。首先需构建包含目标方位角和俯仰角信息的2个空间复合角;然后利用稀疏表示技术求解其中的一个空间复合角,以此作为前提条件,另一个空间复合角就可以解耦为一维波达方向(DOA)估计问题,利用矩阵运算可以求解出来;最后根据已求解的2个空间复合角对方位角和俯仰角进行配对求解。与现有算法相比较,所提方法受快拍数的影响较小,在信噪比较高、角度间隔较大的情况下,具有良好的性能。     

Self-learning-based post-processing for image/video deblocking via sparse representation

Blocking artifact, characterized by visually noticeable changes in pixel values along block boundaries, is a common problem in block-based image/video compression, especially at low bitrate coding. Various post-processing techniques have been proposed to reduce blocking artifacts, but they usually introduce excessive blurring or ringing effects. This paper proposes a self-learning-based post-processing framework for image/video deblocking by properly formulating deblocking as an MCA (morphological component analysis)-based image decomposition problem via sparse representation. Without the need of any prior knowledge (e.g., the positions where blocking artifacts occur, the algorithm used for compression, or the characteristics of image to be processed) about the blocking artifacts to be removed, the proposed framework can automatically learn two dictionaries for decomposing an input decoded image into its “blocking component” and “non-blocking component.” More specifically, the proposed method first decomposes a frame into the low-frequency and high-frequency parts by applying BM3D (block-matching and 3D filtering) algorithm. The high-frequency part is then decomposed into a blocking component and a non-blocking component by performing dictionary learning and sparse coding based on MCA. As a result, the blocking component can be removed from the image/video frame successfully while preserving most original visual details. Experimental results demonstrate the efficacy of the proposed algorithm.