By applying smoothed l0norm（SL0）algorithm,a block compressive sensing（BCS）algorithm called BCS-SL0 is proposed,which deploys SL0 and smoothing filter for image reconstruction.Furthermore,BCS-ReSL0 algorithm is dev...By applying smoothed l0norm（SL0）algorithm,a block compressive sensing（BCS）algorithm called BCS-SL0 is proposed,which deploys SL0 and smoothing filter for image reconstruction.Furthermore,BCS-ReSL0 algorithm is developed to use regularized SL0（ReSL0）in a reconstruction process to deal with noisy situations.The study shows that the proposed BCS-SL0 takes less execution time than the classical BCS with smoothed projected Landweber（BCS-SPL）algorithm in low measurement ratio,while achieving comparable reconstruction quality,and improving the blocking artifacts especially.The experiment results also verify that the reconstruction performance of BCS-ReSL0 is better than that of the BCSSPL in terms of noise tolerance at low measurement ratio.展开更多
Motion deblurring is a basic problem in the field of image processing and analysis. This paper proposes a new method of single image blind deblurring which can be significant to kernel estimation and non-blind deconvo...Motion deblurring is a basic problem in the field of image processing and analysis. This paper proposes a new method of single image blind deblurring which can be significant to kernel estimation and non-blind deconvolution. Experiments show that the details of the image destroy the structure of the kernel, especially when the blur kernel is large. So we extract the image structure with salient edges by the method based on RTV. In addition, the traditional method for motion blur kernel estimation based on sparse priors is conducive to gain a sparse blur kernel. But these priors do not ensure the continuity of blur kernel and sometimes induce noisy estimated results. Therefore we propose the kernel refinement method based on L0 to overcome the above shortcomings. In terms of non-blind deconvolution we adopt the L1/L2 regularization term. Compared with the traditional method, the method based on L1/L2 norm has better adaptability to image structure, and the constructed energy functional can better describe the sharp image. For this model, an effective algorithm is presented based on alternating minimization algorithm.展开更多
In the medical computer tomography （CT） field, total variation （TV）, which is the l1-norm of the discrete gradient transform （DGT）, is widely used as regularization based on the compressive sensing （CS） theory...In the medical computer tomography （CT） field, total variation （TV）, which is the l1-norm of the discrete gradient transform （DGT）, is widely used as regularization based on the compressive sensing （CS） theory. To overcome the TV model＇s disadvantageous tendency of uniformly penalizing the image gradient and over smoothing the low-contrast structures, an iterative algorithm based on the l0-norm optimization of the DGT is proposed. In order to rise to the challenges introduced by the l0-norm DGT, the algorithm uses a pseudo-inverse transform of DGT and adapts an iterative hard thresholding （IHT） algorithm, whose convergence and effective efficiency have been theoretically proven. The simulation demonstrates our conclusions and indicates that the algorithm proposed in this paper can obviously improve the reconstruction quality.展开更多
虚假数据攻击利用输电网状态估计中基于残差的不良数据检测漏洞,通过向数据采集与监控系统(supervisory control and data acquisition,SCADA)系统中注入虚假数据,达到修改电力系统的量测值和状态变量、控制电力系统的运行状态或者获取...虚假数据攻击利用输电网状态估计中基于残差的不良数据检测漏洞,通过向数据采集与监控系统(supervisory control and data acquisition,SCADA)系统中注入虚假数据,达到修改电力系统的量测值和状态变量、控制电力系统的运行状态或者获取经济利益等不法目的。阐述了虚假数据攻击的基本理论和实现机制,并从攻击方法和防御策略、电力系统信息完整性、基于传统虚假数据攻击(false data injection attacks,FDIAs)扩展的攻击方式和攻击向量优化算法4个方面梳理了虚假数据攻击的研究现状和发展情况,分析了现有研究成果的优点和不足。在此基础上,从虚假数据攻击对分布式状态估计的影响、相量测量单元(phasor measurementunit,PMU)/SCADA混合量测下虚假数据攻击和多代理技术在虚假数据攻击防御中的应用3个方面对虚假数据攻击研究进行了展望。展开更多
基金Supported by the National Natural Science Foundation of China(61421001,61331021,61501029)
文摘By applying smoothed l0norm（SL0）algorithm,a block compressive sensing（BCS）algorithm called BCS-SL0 is proposed,which deploys SL0 and smoothing filter for image reconstruction.Furthermore,BCS-ReSL0 algorithm is developed to use regularized SL0（ReSL0）in a reconstruction process to deal with noisy situations.The study shows that the proposed BCS-SL0 takes less execution time than the classical BCS with smoothed projected Landweber（BCS-SPL）algorithm in low measurement ratio,while achieving comparable reconstruction quality,and improving the blocking artifacts especially.The experiment results also verify that the reconstruction performance of BCS-ReSL0 is better than that of the BCSSPL in terms of noise tolerance at low measurement ratio.
基金Partially Supported by National Natural Science Foundation of China(No.61173102)
文摘Motion deblurring is a basic problem in the field of image processing and analysis. This paper proposes a new method of single image blind deblurring which can be significant to kernel estimation and non-blind deconvolution. Experiments show that the details of the image destroy the structure of the kernel, especially when the blur kernel is large. So we extract the image structure with salient edges by the method based on RTV. In addition, the traditional method for motion blur kernel estimation based on sparse priors is conducive to gain a sparse blur kernel. But these priors do not ensure the continuity of blur kernel and sometimes induce noisy estimated results. Therefore we propose the kernel refinement method based on L0 to overcome the above shortcomings. In terms of non-blind deconvolution we adopt the L1/L2 regularization term. Compared with the traditional method, the method based on L1/L2 norm has better adaptability to image structure, and the constructed energy functional can better describe the sharp image. For this model, an effective algorithm is presented based on alternating minimization algorithm.
文摘In the medical computer tomography （CT） field, total variation （TV）, which is the l1-norm of the discrete gradient transform （DGT）, is widely used as regularization based on the compressive sensing （CS） theory. To overcome the TV model＇s disadvantageous tendency of uniformly penalizing the image gradient and over smoothing the low-contrast structures, an iterative algorithm based on the l0-norm optimization of the DGT is proposed. In order to rise to the challenges introduced by the l0-norm DGT, the algorithm uses a pseudo-inverse transform of DGT and adapts an iterative hard thresholding （IHT） algorithm, whose convergence and effective efficiency have been theoretically proven. The simulation demonstrates our conclusions and indicates that the algorithm proposed in this paper can obviously improve the reconstruction quality.
文摘虚假数据攻击利用输电网状态估计中基于残差的不良数据检测漏洞,通过向数据采集与监控系统(supervisory control and data acquisition,SCADA)系统中注入虚假数据,达到修改电力系统的量测值和状态变量、控制电力系统的运行状态或者获取经济利益等不法目的。阐述了虚假数据攻击的基本理论和实现机制,并从攻击方法和防御策略、电力系统信息完整性、基于传统虚假数据攻击(false data injection attacks,FDIAs)扩展的攻击方式和攻击向量优化算法4个方面梳理了虚假数据攻击的研究现状和发展情况,分析了现有研究成果的优点和不足。在此基础上,从虚假数据攻击对分布式状态估计的影响、相量测量单元(phasor measurementunit,PMU)/SCADA混合量测下虚假数据攻击和多代理技术在虚假数据攻击防御中的应用3个方面对虚假数据攻击研究进行了展望。
