Image denoising is a well-studied problem closely related to sparse coding. Noticing that the Laplacian distribution has a strong sparseness, we use Laplacian scale mixture to model sparse coefficients. With the obser...Image denoising is a well-studied problem closely related to sparse coding. Noticing that the Laplacian distribution has a strong sparseness, we use Laplacian scale mixture to model sparse coefficients. With the observation that prior information of an image is relevant to the estimation of sparse coefficients, we introduce the prior information into maximum a posteriori（MAP） estimation of sparse coefficients by an appropriate estimate of the probability density function. Extending to structured sparsity, a nonlocal image denoising model： Improved Simultaneous Sparse Coding with Laplacian Scale Mixture（ISSC-LSM） is proposed. The centering preprocessing, which admits biased-mean of sparse coefficients and saves expensive computation, is done firstly. By alternating minimization and learning an orthogonal PCA dictionary, an efficient algorithm with closed-form solutions is proposed. When applied to noise removal, our proposed ISSC-LSM can capture structured image features, and the adoption of image prior information leads to highly competitive denoising performance. Experimental results show that the proposed method often provides higher subjective and objective qualities than other competing approaches. Our method is most suitable for processing images with abundant self-repeating patterns by effectively suppressing undesirable artifacts while maintaining the textures and edges.展开更多
In this paper,a sparse graph neural network-aided(SGNN-aided)decoder is proposed for improving the decoding performance of polar codes under bursty interference.Firstly,a sparse factor graph is constructed using the e...In this paper,a sparse graph neural network-aided(SGNN-aided)decoder is proposed for improving the decoding performance of polar codes under bursty interference.Firstly,a sparse factor graph is constructed using the encoding characteristic to achieve high-throughput polar decoding.To further improve the decoding performance,a residual gated bipartite graph neural network is designed for updating embedding vectors of heterogeneous nodes based on a bidirectional message passing neural network.This framework exploits gated recurrent units and residual blocks to address the gradient disappearance in deep graph recurrent neural networks.Finally,predictions are generated by feeding the embedding vectors into a readout module.Simulation results show that the proposed decoder is more robust than the existing ones in the presence of bursty interference and exhibits high universality.展开更多
Channel state information(CSI)is very important to sparse code multiple access combined with orthogonal frequency division multiplexing(SCMA-OFDM)systems for data detection.The main goal of this paper is to tackle the...Channel state information(CSI)is very important to sparse code multiple access combined with orthogonal frequency division multiplexing(SCMA-OFDM)systems for data detection.The main goal of this paper is to tackle the computational complexity and pilot overhead issues when estima-ting and tracking the channel frequency response of each user in uplink SCMA-OFDM systems.To this end,a new binary pilot structure is first designed to realize the initial channel estimation with significantly reduced computational complexity.Then,a channel tracking method is proposed to update the channel estimation in time-varying channels,which exploits a modified least mean square(LMS)technique with the feedback from the detector.Simulation results show that the pro-posed pilot structure can provide accurate channel estimation results.Moreover,the average bit error rate(BER)performance of the modified LMS algorithm can approach that of a detector with perfect CSI within 2 dB at the normalized Doppler frequency up to 6×10^(-6).展开更多
Sparse code multiple access(SCMA)is a non-orthogonal multiple access(NOMA)scheme based on joint modulation and spread spectrum coding.It is ideal for future communication networks with a massive number of nodes due to...Sparse code multiple access(SCMA)is a non-orthogonal multiple access(NOMA)scheme based on joint modulation and spread spectrum coding.It is ideal for future communication networks with a massive number of nodes due to its ability to handle user overload.Introducing SCMA into visible light communication(VLC)systems can improve the data transmission capability of the system.However,designing a suitable codebook becomes a challenging problem when addressing the demands of massive connectivity scenarios.Therefore,this paper proposes a low-complexity design method for high-overload codebooks based on the minimum bit error rate(BER)criterion.Firstly,this paper constructs a new codebook with parameters based on the symmetric mother codebook structure by allocating the codeword power so that the power of each user codebook is unbalanced;then,the BER performance in the visible light communication system is optimized to obtain specific parameters;finally,the successive interference cancellation(SIC)detection algorithm is used at the receiver side.Simulation results show that the method proposed in this paper can converge quickly by utilizing a relatively small number of detection iterations.This can simultaneously reduce the complexity of design and detection,outperforming existing design methods for massive SCMA codebooks.展开更多
This paper proposes a class of novel progressive edge growth-based codebooks for downlink sparse code multiple access(SCMA)systems.In the first scheme,we propose to progressively design the codebooks of each resource ...This paper proposes a class of novel progressive edge growth-based codebooks for downlink sparse code multiple access(SCMA)systems.In the first scheme,we propose to progressively design the codebooks of each resource node(RN)instead of rotating a mother constellation(MC)as in the conventional SCMA works.In the other one,based on the MC,a multi-resources rotated codebooks are proposed to improve the performance of the superimposed constellations.The resultant codebooks are respectively referred to as the resource edge multidimensional codebooks(REMC)and the user edge multi-dimensional codebooks(UEMC).Additionally,we delve into the detailed design of the MC and the superimposed constellation.Then,we pay special attention to the application of the proposed schemes to challenging design cases,particularly for the high dimensional,high rate,and irregular codebooks,where the corresponding simplified schemes are proposed to reduce the complexity of codebook design.Finally,simulation results are presented to demonstrate the superiority of our progressive edge growth-based schemes.The numerical results indicate that the proposed codebooks significantly outperform the stateof-the-art codebooks.In addition,we also show that the proposed REMC codebooks outperform in the lower signal-to-noise ratio(SNR)regime,whereas the UEMC codebooks exhibit better performance at higher SNRs.展开更多
卫星物联网是6G实现万物智联的关键所在,而其频谱资源和星上载荷的双重受限性,给海量用户的接入效能提升带来严峻挑战。针对稀疏码多址接入(SCMA,sparse code multiple access)星载接收机多用户检测效率低下问题,考虑迭代过程中码字发...卫星物联网是6G实现万物智联的关键所在,而其频谱资源和星上载荷的双重受限性,给海量用户的接入效能提升带来严峻挑战。针对稀疏码多址接入(SCMA,sparse code multiple access)星载接收机多用户检测效率低下问题,考虑迭代过程中码字发送概率的差异性,提出一种基于状态位置信息的对数域消息传递算法(SPI-Log-MPA,state position information based log message passing algorithm)。该算法根据用户码字状态位置的变化情况,在迭代检测过程中通过减少不可靠码字、提前对稳定用户进行解码、设立奖惩机制对非稳定用户进行解码等措施,显著提升了检测效率。在此基础上,对阶段设置与状态位置信息矩阵两方面进行优化,提出两阶段的改进算法,进一步加快了收敛速度。复杂度分析与仿真结果表明,所提算法在保证误码率性能的前提下具有更低的计算复杂度。展开更多
A two-level Bregmanized method with graph regularized sparse coding （TBGSC） is presented for image interpolation. The outer-level Bregman iterative procedure enforces the observation data constraints, while the inne...A two-level Bregmanized method with graph regularized sparse coding （TBGSC） is presented for image interpolation. The outer-level Bregman iterative procedure enforces the observation data constraints, while the inner-level Bregmanized method devotes to dictionary updating and sparse represention of small overlapping image patches. The introduced constraint of graph regularized sparse coding can capture local image features effectively, and consequently enables accurate reconstruction from highly undersampled partial data. Furthermore, modified sparse coding and simple dictionary updating applied in the inner minimization make the proposed algorithm converge within a relatively small number of iterations. Experimental results demonstrate that the proposed algorithm can effectively reconstruct images and it outperforms the current state-of-the-art approaches in terms of visual comparisons and quantitative measures.展开更多
Impulse components in vibration signals are important fault features of complex machines. Sparse coding （SC） algorithm has been introduced as an impulse feature extraction method, but it could not guarantee a satisf...Impulse components in vibration signals are important fault features of complex machines. Sparse coding （SC） algorithm has been introduced as an impulse feature extraction method, but it could not guarantee a satisfactory performance in processing vibration signals with heavy background noises. In this paper, a method based on fusion sparse coding （FSC） and online dictionary learning is proposed to extract impulses efficiently. Firstly, fusion scheme of different sparse coding algorithms is presented to ensure higher reconstruction accuracy. Then, an improved online dictionary learning method using FSC scheme is established to obtain redundant dictionary and it can capture specific features of training samples and reconstruct the sparse approximation of vibration signals. Simulation shows that this method has a good performance in solving sparse coefficients and training redundant dictionary compared with other methods. Lastly, the proposed method is further applied to processing aircraft engine rotor vibration signals. Compared with other feature extraction approaches, our method can extract impulse features accurately and efficiently from heavy noisy vibration signal, which has significant supports for machinery fault detection and diagnosis.展开更多
A new method for the construction of the high performance systematic irregular low-density paritycheck （LDPC） codes based on the sparse generator matrix （G-LDPC） is introduced. The code can greatly reduce the enco...A new method for the construction of the high performance systematic irregular low-density paritycheck （LDPC） codes based on the sparse generator matrix （G-LDPC） is introduced. The code can greatly reduce the encoding complexity while maintaining the same decoding complexity as traditional regular LDPC （H-LDPC） codes defined by the sparse parity check matrix. Simulation results show that the performance of the proposed irregular LDPC codes can offer significant gains over traditional LDPC codes in low SNRs with a few decoding iterations over an additive white Gaussian noise （AWGN） channel.展开更多
For quantum sparse graph codes with stabilizer formalism, the unavoidable girth-four cycles in their Tanner graphs greatly degrade the iterative decoding performance with standard belief-propagation （BP） algorithm. ...For quantum sparse graph codes with stabilizer formalism, the unavoidable girth-four cycles in their Tanner graphs greatly degrade the iterative decoding performance with standard belief-propagation （BP） algorithm. In this paper, we present a jointly-check iterative algorithm suitable for decoding quantum sparse graph codes efficiently. Numerical simulations show that this modified method outperforms standard BP algorithm with an obvious performance improvement.展开更多
Due to the strong background noise and the acquisition system noise,the useful characteristics are often difficult to be detected.To solve this problem,sparse coding captures a concise representation of the high-level...Due to the strong background noise and the acquisition system noise,the useful characteristics are often difficult to be detected.To solve this problem,sparse coding captures a concise representation of the high-level features in the signal using the underlying structure of the signal.Recently,an Online Convolutional Sparse Coding(OCSC)denoising algorithm has been proposed.However,it does not consider the structural characteristics of the signal,the sparsity of each iteration is not enough.Therefore,a threshold shrinkage algorithm considering neighborhood sparsity is proposed,and a training strategy from loose to tight is developed to further improve the denoising performance of the algorithm,called Variable Threshold Neighborhood Online Convolution Sparse Coding(VTNOCSC).By embedding the structural sparse threshold shrinkage operator into the process of solving the sparse coefficient and gradually approaching the optimal noise separation point in the training,the signal denoising performance of the algorithm is greatly improved.VTNOCSC is used to process the actual bearing fault signal,the noise interference is successfully reduced and the interest features are more evident.Compared with other existing methods,VTNOCSC has better denoising performance.展开更多
Key frame extraction based on sparse coding can reduce the redundancy of continuous frames and concisely express the entire video.However,how to develop a key frame extraction algorithm that can automatically extract ...Key frame extraction based on sparse coding can reduce the redundancy of continuous frames and concisely express the entire video.However,how to develop a key frame extraction algorithm that can automatically extract a few frames with a low reconstruction error remains a challenge.In this paper,we propose a novel model of structured sparse-codingbased key frame extraction,wherein a nonconvex group log-regularizer is used with strong sparsity and a low reconstruction error.To automatically extract key frames,a decomposition scheme is designed to separate the sparse coefficient matrix by rows.The rows enforced by the nonconvex group log-regularizer become zero or nonzero,leading to the learning of the structured sparse coefficient matrix.To solve the nonconvex problems due to the log-regularizer,the difference of convex algorithm(DCA)is employed to decompose the log-regularizer into the difference of two convex functions related to the l1 norm,which can be directly obtained through the proximal operator.Therefore,an efficient structured sparse coding algorithm with the group log-regularizer for key frame extraction is developed,which can automatically extract a few frames directly from the video to represent the entire video with a low reconstruction error.Experimental results demonstrate that the proposed algorithm can extract more accurate key frames from most Sum Me videos compared to the stateof-the-art methods.Furthermore,the proposed algorithm can obtain a higher compression with a nearly 18% increase compared to sparse modeling representation selection(SMRS)and an 8% increase compared to SC-det on the VSUMM dataset.展开更多
The imaging speed is a bottleneck for magnetic resonance imaging( MRI) since it appears. To alleviate this difficulty,a novel graph regularized sparse coding method for highly undersampled MRI reconstruction( GSCMRI) ...The imaging speed is a bottleneck for magnetic resonance imaging( MRI) since it appears. To alleviate this difficulty,a novel graph regularized sparse coding method for highly undersampled MRI reconstruction( GSCMRI) was proposed. The graph regularized sparse coding showed the potential in maintaining the geometrical information of the data. In this study, it was incorporated with two-level Bregman iterative procedure that updated the data term in outer-level and learned dictionary in innerlevel. Moreover,the graph regularized sparse coding and simple dictionary updating stages derived by the inner minimization made the proposed algorithm converge in few iterations, meanwhile achieving superior reconstruction performance. Extensive experimental results have demonstrated GSCMRI can consistently recover both real-valued MR images and complex-valued MR data efficiently,and outperform the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.展开更多
Abstract:Sparse coding(SC)based visual tracking(l1-tracker)is gaining increasing attention,and many related algorithms are developed.In these algorithms,each candidate region is sparsely represented as a set of target...Abstract:Sparse coding(SC)based visual tracking(l1-tracker)is gaining increasing attention,and many related algorithms are developed.In these algorithms,each candidate region is sparsely represented as a set of target templates.However,the structure connecting these candidate regions is usually ignored.Lu proposed an NLSSC-tracker with non-local self-similarity sparse coding to address this issue,which has a high computational cost.In this study,we propose an Euclidean local-structure constraint based sparse coding tracker with a smoothed Euclidean local structure.With this tracker,the optimization procedure is transformed to a small-scale l1-optimization problem,significantly reducing the computational cost.Extensive experimental results on visual tracking demonstrate the eectiveness and efficiency of the proposed algorithm.展开更多
Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar （ISAR） target recognition, this paper imports the concept of visual perception and presents a novel method, which is ba...Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar （ISAR） target recognition, this paper imports the concept of visual perception and presents a novel method, which is based on the combination of non-negative sparse coding （NNSC） and linear discrimination optimization, to recognize targets in ISAR images. This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training, to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers. Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors, which are input into k neighbors classifier to realize recognition finally. The feasibility and robustness of the proposed method are proved by comparing with the template matching, principle component analysis （PCA） and non-negative matrix factorization （NMF） via simulations.展开更多
基金Supported by the National Natural Science Foundation of China(61573014)
文摘Image denoising is a well-studied problem closely related to sparse coding. Noticing that the Laplacian distribution has a strong sparseness, we use Laplacian scale mixture to model sparse coefficients. With the observation that prior information of an image is relevant to the estimation of sparse coefficients, we introduce the prior information into maximum a posteriori（MAP） estimation of sparse coefficients by an appropriate estimate of the probability density function. Extending to structured sparsity, a nonlocal image denoising model： Improved Simultaneous Sparse Coding with Laplacian Scale Mixture（ISSC-LSM） is proposed. The centering preprocessing, which admits biased-mean of sparse coefficients and saves expensive computation, is done firstly. By alternating minimization and learning an orthogonal PCA dictionary, an efficient algorithm with closed-form solutions is proposed. When applied to noise removal, our proposed ISSC-LSM can capture structured image features, and the adoption of image prior information leads to highly competitive denoising performance. Experimental results show that the proposed method often provides higher subjective and objective qualities than other competing approaches. Our method is most suitable for processing images with abundant self-repeating patterns by effectively suppressing undesirable artifacts while maintaining the textures and edges.
文摘In this paper,a sparse graph neural network-aided(SGNN-aided)decoder is proposed for improving the decoding performance of polar codes under bursty interference.Firstly,a sparse factor graph is constructed using the encoding characteristic to achieve high-throughput polar decoding.To further improve the decoding performance,a residual gated bipartite graph neural network is designed for updating embedding vectors of heterogeneous nodes based on a bidirectional message passing neural network.This framework exploits gated recurrent units and residual blocks to address the gradient disappearance in deep graph recurrent neural networks.Finally,predictions are generated by feeding the embedding vectors into a readout module.Simulation results show that the proposed decoder is more robust than the existing ones in the presence of bursty interference and exhibits high universality.
基金Supported by the National Natural Science Foundation of China(No.62171135)the Natural Science Foundation of Fujian Province(No.2023J01399)。
文摘Channel state information(CSI)is very important to sparse code multiple access combined with orthogonal frequency division multiplexing(SCMA-OFDM)systems for data detection.The main goal of this paper is to tackle the computational complexity and pilot overhead issues when estima-ting and tracking the channel frequency response of each user in uplink SCMA-OFDM systems.To this end,a new binary pilot structure is first designed to realize the initial channel estimation with significantly reduced computational complexity.Then,a channel tracking method is proposed to update the channel estimation in time-varying channels,which exploits a modified least mean square(LMS)technique with the feedback from the detector.Simulation results show that the pro-posed pilot structure can provide accurate channel estimation results.Moreover,the average bit error rate(BER)performance of the modified LMS algorithm can approach that of a detector with perfect CSI within 2 dB at the normalized Doppler frequency up to 6×10^(-6).
基金supported in part by the National Science Foundation of China(NSFC)under Grant 62161024Jiangxi Provincial Natural Science Foundation under Grant 20224BAB212002+3 种基金Jiangxi Provincial Talent Project for Academic and Technical Leaders of Major Disciplines under Grant 20232BCJ23085,China Postdoctoral Science Foundation under Grant 2021TQ0136 and 2022M711463the State Key Laboratory of Computer Architecture(ICT,CAS)Open Project under Grant CARCHB202019supported in part by the National Natural Science Foundation of China(NSFC)under Grant 62061030supported in part by the National Natural Science Foundation of China(NSFC)under Grant 62161023。
文摘Sparse code multiple access(SCMA)is a non-orthogonal multiple access(NOMA)scheme based on joint modulation and spread spectrum coding.It is ideal for future communication networks with a massive number of nodes due to its ability to handle user overload.Introducing SCMA into visible light communication(VLC)systems can improve the data transmission capability of the system.However,designing a suitable codebook becomes a challenging problem when addressing the demands of massive connectivity scenarios.Therefore,this paper proposes a low-complexity design method for high-overload codebooks based on the minimum bit error rate(BER)criterion.Firstly,this paper constructs a new codebook with parameters based on the symmetric mother codebook structure by allocating the codeword power so that the power of each user codebook is unbalanced;then,the BER performance in the visible light communication system is optimized to obtain specific parameters;finally,the successive interference cancellation(SIC)detection algorithm is used at the receiver side.Simulation results show that the method proposed in this paper can converge quickly by utilizing a relatively small number of detection iterations.This can simultaneously reduce the complexity of design and detection,outperforming existing design methods for massive SCMA codebooks.
文摘This paper proposes a class of novel progressive edge growth-based codebooks for downlink sparse code multiple access(SCMA)systems.In the first scheme,we propose to progressively design the codebooks of each resource node(RN)instead of rotating a mother constellation(MC)as in the conventional SCMA works.In the other one,based on the MC,a multi-resources rotated codebooks are proposed to improve the performance of the superimposed constellations.The resultant codebooks are respectively referred to as the resource edge multidimensional codebooks(REMC)and the user edge multi-dimensional codebooks(UEMC).Additionally,we delve into the detailed design of the MC and the superimposed constellation.Then,we pay special attention to the application of the proposed schemes to challenging design cases,particularly for the high dimensional,high rate,and irregular codebooks,where the corresponding simplified schemes are proposed to reduce the complexity of codebook design.Finally,simulation results are presented to demonstrate the superiority of our progressive edge growth-based schemes.The numerical results indicate that the proposed codebooks significantly outperform the stateof-the-art codebooks.In addition,we also show that the proposed REMC codebooks outperform in the lower signal-to-noise ratio(SNR)regime,whereas the UEMC codebooks exhibit better performance at higher SNRs.
文摘卫星物联网是6G实现万物智联的关键所在,而其频谱资源和星上载荷的双重受限性,给海量用户的接入效能提升带来严峻挑战。针对稀疏码多址接入(SCMA,sparse code multiple access)星载接收机多用户检测效率低下问题,考虑迭代过程中码字发送概率的差异性,提出一种基于状态位置信息的对数域消息传递算法(SPI-Log-MPA,state position information based log message passing algorithm)。该算法根据用户码字状态位置的变化情况,在迭代检测过程中通过减少不可靠码字、提前对稳定用户进行解码、设立奖惩机制对非稳定用户进行解码等措施,显著提升了检测效率。在此基础上,对阶段设置与状态位置信息矩阵两方面进行优化,提出两阶段的改进算法,进一步加快了收敛速度。复杂度分析与仿真结果表明,所提算法在保证误码率性能的前提下具有更低的计算复杂度。
基金The National Natural Science Foundation of China (No.61362001,61102043,61262084,20132BAB211030,20122BAB211015)the Basic Research Program of Shenzhen(No.JC201104220219A)
文摘A two-level Bregmanized method with graph regularized sparse coding （TBGSC） is presented for image interpolation. The outer-level Bregman iterative procedure enforces the observation data constraints, while the inner-level Bregmanized method devotes to dictionary updating and sparse represention of small overlapping image patches. The introduced constraint of graph regularized sparse coding can capture local image features effectively, and consequently enables accurate reconstruction from highly undersampled partial data. Furthermore, modified sparse coding and simple dictionary updating applied in the inner minimization make the proposed algorithm converge within a relatively small number of iterations. Experimental results demonstrate that the proposed algorithm can effectively reconstruct images and it outperforms the current state-of-the-art approaches in terms of visual comparisons and quantitative measures.
基金supported by the National Natural Science Foundation of China (No. 51201182)
文摘Impulse components in vibration signals are important fault features of complex machines. Sparse coding （SC） algorithm has been introduced as an impulse feature extraction method, but it could not guarantee a satisfactory performance in processing vibration signals with heavy background noises. In this paper, a method based on fusion sparse coding （FSC） and online dictionary learning is proposed to extract impulses efficiently. Firstly, fusion scheme of different sparse coding algorithms is presented to ensure higher reconstruction accuracy. Then, an improved online dictionary learning method using FSC scheme is established to obtain redundant dictionary and it can capture specific features of training samples and reconstruct the sparse approximation of vibration signals. Simulation shows that this method has a good performance in solving sparse coefficients and training redundant dictionary compared with other methods. Lastly, the proposed method is further applied to processing aircraft engine rotor vibration signals. Compared with other feature extraction approaches, our method can extract impulse features accurately and efficiently from heavy noisy vibration signal, which has significant supports for machinery fault detection and diagnosis.
文摘A new method for the construction of the high performance systematic irregular low-density paritycheck （LDPC） codes based on the sparse generator matrix （G-LDPC） is introduced. The code can greatly reduce the encoding complexity while maintaining the same decoding complexity as traditional regular LDPC （H-LDPC） codes defined by the sparse parity check matrix. Simulation results show that the performance of the proposed irregular LDPC codes can offer significant gains over traditional LDPC codes in low SNRs with a few decoding iterations over an additive white Gaussian noise （AWGN） channel.
基金Project supported by the National Natural Science Foundation of China(Grant No.60972046)Grant from the National Defense Pre-Research Foundation of China
文摘For quantum sparse graph codes with stabilizer formalism, the unavoidable girth-four cycles in their Tanner graphs greatly degrade the iterative decoding performance with standard belief-propagation （BP） algorithm. In this paper, we present a jointly-check iterative algorithm suitable for decoding quantum sparse graph codes efficiently. Numerical simulations show that this modified method outperforms standard BP algorithm with an obvious performance improvement.
基金supported by the National Key Research and Development Program of China(No.2018YFB2003300)National Science and Technology Major Project,China(No.2017-IV-0008-0045)National Natural Science Foundation of China(No.51675262).
文摘Due to the strong background noise and the acquisition system noise,the useful characteristics are often difficult to be detected.To solve this problem,sparse coding captures a concise representation of the high-level features in the signal using the underlying structure of the signal.Recently,an Online Convolutional Sparse Coding(OCSC)denoising algorithm has been proposed.However,it does not consider the structural characteristics of the signal,the sparsity of each iteration is not enough.Therefore,a threshold shrinkage algorithm considering neighborhood sparsity is proposed,and a training strategy from loose to tight is developed to further improve the denoising performance of the algorithm,called Variable Threshold Neighborhood Online Convolution Sparse Coding(VTNOCSC).By embedding the structural sparse threshold shrinkage operator into the process of solving the sparse coefficient and gradually approaching the optimal noise separation point in the training,the signal denoising performance of the algorithm is greatly improved.VTNOCSC is used to process the actual bearing fault signal,the noise interference is successfully reduced and the interest features are more evident.Compared with other existing methods,VTNOCSC has better denoising performance.
基金supported in part by the National Natural Science Foundation of China(61903090,61727810,62073086,62076077,61803096,U191140003)the Guangzhou Science and Technology Program Project(202002030289)Japan Society for the Promotion of Science(JSPS)KAKENHI(18K18083)。
文摘Key frame extraction based on sparse coding can reduce the redundancy of continuous frames and concisely express the entire video.However,how to develop a key frame extraction algorithm that can automatically extract a few frames with a low reconstruction error remains a challenge.In this paper,we propose a novel model of structured sparse-codingbased key frame extraction,wherein a nonconvex group log-regularizer is used with strong sparsity and a low reconstruction error.To automatically extract key frames,a decomposition scheme is designed to separate the sparse coefficient matrix by rows.The rows enforced by the nonconvex group log-regularizer become zero or nonzero,leading to the learning of the structured sparse coefficient matrix.To solve the nonconvex problems due to the log-regularizer,the difference of convex algorithm(DCA)is employed to decompose the log-regularizer into the difference of two convex functions related to the l1 norm,which can be directly obtained through the proximal operator.Therefore,an efficient structured sparse coding algorithm with the group log-regularizer for key frame extraction is developed,which can automatically extract a few frames directly from the video to represent the entire video with a low reconstruction error.Experimental results demonstrate that the proposed algorithm can extract more accurate key frames from most Sum Me videos compared to the stateof-the-art methods.Furthermore,the proposed algorithm can obtain a higher compression with a nearly 18% increase compared to sparse modeling representation selection(SMRS)and an 8% increase compared to SC-det on the VSUMM dataset.
基金National Natural Science Foundations of China(Nos.61362001,61102043,61262084)Technology Foundations of Department of Education of Jiangxi Province,China(Nos.GJJ12006,GJJ14196)Natural Science Foundations of Jiangxi Province,China(Nos.20132BAB211030,20122BAB211015)
文摘The imaging speed is a bottleneck for magnetic resonance imaging( MRI) since it appears. To alleviate this difficulty,a novel graph regularized sparse coding method for highly undersampled MRI reconstruction( GSCMRI) was proposed. The graph regularized sparse coding showed the potential in maintaining the geometrical information of the data. In this study, it was incorporated with two-level Bregman iterative procedure that updated the data term in outer-level and learned dictionary in innerlevel. Moreover,the graph regularized sparse coding and simple dictionary updating stages derived by the inner minimization made the proposed algorithm converge in few iterations, meanwhile achieving superior reconstruction performance. Extensive experimental results have demonstrated GSCMRI can consistently recover both real-valued MR images and complex-valued MR data efficiently,and outperform the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.
基金National Natural Foundation of China under Grant(61572085,61502058)
文摘Abstract:Sparse coding(SC)based visual tracking(l1-tracker)is gaining increasing attention,and many related algorithms are developed.In these algorithms,each candidate region is sparsely represented as a set of target templates.However,the structure connecting these candidate regions is usually ignored.Lu proposed an NLSSC-tracker with non-local self-similarity sparse coding to address this issue,which has a high computational cost.In this study,we propose an Euclidean local-structure constraint based sparse coding tracker with a smoothed Euclidean local structure.With this tracker,the optimization procedure is transformed to a small-scale l1-optimization problem,significantly reducing the computational cost.Extensive experimental results on visual tracking demonstrate the eectiveness and efficiency of the proposed algorithm.
基金supported by the Prominent Youth Fund of the National Natural Science Foundation of China (61025006)
文摘Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar （ISAR） target recognition, this paper imports the concept of visual perception and presents a novel method, which is based on the combination of non-negative sparse coding （NNSC） and linear discrimination optimization, to recognize targets in ISAR images. This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training, to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers. Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors, which are input into k neighbors classifier to realize recognition finally. The feasibility and robustness of the proposed method are proved by comparing with the template matching, principle component analysis （PCA） and non-negative matrix factorization （NMF） via simulations.
