提出一种基于图像残差的超分辨率重建算法。以原高分辨率图像与插值放大后图像之间的图像残差与低分辨率图像样本特征作为样本对,对其进行K均值分类,并对每类样本对采用KSVD(K-singular value decomposi-tion)方法进行训练获得高、低分...提出一种基于图像残差的超分辨率重建算法。以原高分辨率图像与插值放大后图像之间的图像残差与低分辨率图像样本特征作为样本对,对其进行K均值分类,并对每类样本对采用KSVD(K-singular value decomposi-tion)方法进行训练获得高、低分辨率字典对,然后根据测试样本与类中心的欧氏距离选择字典对,以与测试样本相近的多个类别所重建的结果加权获得图像残差,并结合低分辨率图像的插值结果获得高分辨率图像。实验结果表明,提出的方法具有更高的重建质量,且采用训练样本分类和相近类别的重建结果的加权和有利于提高图像重建质量。展开更多
针对以往稀疏编码在图像去噪过程中存在的噪声残留和缺乏对图像的边缘与细节的本质特征的保护等问题,提出了一种结合第二代Bandelet变换分块的字典学习图像去噪算法,其更好地利用了图像的几何特性进行去噪。首先,通过第二代Bandelet变...针对以往稀疏编码在图像去噪过程中存在的噪声残留和缺乏对图像的边缘与细节的本质特征的保护等问题,提出了一种结合第二代Bandelet变换分块的字典学习图像去噪算法,其更好地利用了图像的几何特性进行去噪。首先,通过第二代Bandelet变换可以灵活地根据图像几何流的正则性特征并能够自适应地获得图像的最稀疏表示来准确估计图像信息,并能自适应地选择最优的几何方向;然后,根据K-奇异值分解(K-Singular Value Decomposition,K-SVD)算法来训练学习字典;最后,通过四叉树分割对噪声图像进行自适应分块,从而去除噪声并保护图像的边缘与细节。实验结果表明,相比于其他学习字典,所提算法能更有效地保留图像的边缘特征与图像的精细结构。展开更多
To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modi...To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition(K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bagQoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoSwords, Locality Constrained Feature Coding(LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines(SVM) clas-sifier. Our experimental results demonstrate the feasibility of the proposed classification method.展开更多
The transformation of basic functions is one of the most commonly used techniques for seismic denoising,which employs sparse representation of seismic data in the transform domain. The choice of transform base functio...The transformation of basic functions is one of the most commonly used techniques for seismic denoising,which employs sparse representation of seismic data in the transform domain. The choice of transform base functions has an influence on denoising results. We propose a learning-type overcomplete dictionary based on the K-singular value decomposition( K-SVD) algorithm. To construct the dictionary and use it for random seismic noise attenuation,we replace fixed transform base functions with an overcomplete redundancy function library. Owing to the adaptability to data characteristics,the learning-type dictionary describes essential data characteristics much better than conventional denoising methods. The sparsest representation of signals is obtained by the learning and training of seismic data. By comparing the same seismic data obtained using the learning-type overcomplete dictionary based on K-SVD and the data obtained using other denoising methods,we find that the learning-type overcomplete dictionary based on the K-SVD algorithm represents the seismic data more sparsely,effectively suppressing the random noise and improving the signal-to-noise ratio.展开更多
In distributed radar,most of existing radar networks operate in the tracking fusion mode which combines radar target tracks for a higher positioning accuracy.However,as the filtering covariance matrix indicating posit...In distributed radar,most of existing radar networks operate in the tracking fusion mode which combines radar target tracks for a higher positioning accuracy.However,as the filtering covariance matrix indicating positioning accuracy often occupies many bits,the communication cost from local sensors to the fusion is not always sufficiently low for some wireless communication chan-nels.This paper studies how to compress data for distributed tracking fusion algorithms.Based on the K-singular value decomposition(K-SVD)algorithm,a sparse coding algorithm is presented to sparsely represent the filtering covariance matrix.Then the least square quantization(LSQ)algo-rithm is used to quantize the data according to the statistical characteristics of the sparse coeffi-cients.Quantized results are then coded with an arithmetic coding method which can further com-press data.Numerical results indicate that this tracking data compression algorithm drops the com-munication bandwidth to 4%at the cost of a 16%root mean squared error(RMSE)loss.展开更多
Behind the prevalence of multimedia technology,digital copyright disputes are becoming increasingly serious.The digital watermarking prevention technique against the copyright infringement needs to be improved urgentl...Behind the prevalence of multimedia technology,digital copyright disputes are becoming increasingly serious.The digital watermarking prevention technique against the copyright infringement needs to be improved urgently.Among the proposed technologies,zero-watermarking has been favored recently.In order to improve the robustness of the zero-watermarking,a novel robust audio zerowatermarking method based on sparse representation is proposed.The proposed scheme is mainly based on the K-singular value decomposition(K-SVD)algorithm to construct an optimal over complete dictionary from the background audio signal.After that,the orthogonal matching pursuit(OMP)algorithm is used to calculate the sparse coefficient of the segmented test audio and generate the corresponding sparse coefficient matrix.Then,the mean value of absolute sparse coefficients in the sparse matrix of segmented speech is calculated and selected,and then comparing the mean absolute coefficient of segmented speech with the average value of the selected coefficients to realize the embedding of zero-watermarking.Experimental results show that the proposed audio zerowatermarking algorithm based on sparse representation performs effectively in resisting various common attacks.Compared with the baseline works,the proposed method has better robustness.展开更多
文摘提出一种基于图像残差的超分辨率重建算法。以原高分辨率图像与插值放大后图像之间的图像残差与低分辨率图像样本特征作为样本对,对其进行K均值分类,并对每类样本对采用KSVD(K-singular value decomposi-tion)方法进行训练获得高、低分辨率字典对,然后根据测试样本与类中心的欧氏距离选择字典对,以与测试样本相近的多个类别所重建的结果加权获得图像残差,并结合低分辨率图像的插值结果获得高分辨率图像。实验结果表明,提出的方法具有更高的重建质量,且采用训练样本分类和相近类别的重建结果的加权和有利于提高图像重建质量。
文摘针对以往稀疏编码在图像去噪过程中存在的噪声残留和缺乏对图像的边缘与细节的本质特征的保护等问题,提出了一种结合第二代Bandelet变换分块的字典学习图像去噪算法,其更好地利用了图像的几何特性进行去噪。首先,通过第二代Bandelet变换可以灵活地根据图像几何流的正则性特征并能够自适应地获得图像的最稀疏表示来准确估计图像信息,并能自适应地选择最优的几何方向;然后,根据K-奇异值分解(K-Singular Value Decomposition,K-SVD)算法来训练学习字典;最后,通过四叉树分割对噪声图像进行自适应分块,从而去除噪声并保护图像的边缘与细节。实验结果表明,相比于其他学习字典,所提算法能更有效地保留图像的边缘特征与图像的精细结构。
基金supported in part by the National Natural Science Foundation of China (NO. 61401004, 61271233, 60972038)Plan of introduction and cultivation of university leading talents in Anhui (No.gxfxZ D2016013)+3 种基金the Natural Science Foundation of the Higher Education Institutions of Anhui Province, China (No. KJ2010B357)Startup Project of Anhui Normal University Doctor Scientific Research (No.2016XJJ129)the US National Science Foundation under grants CNS1702957 and ACI-1642133the Wireless Engineering Research and Education Center at Auburn University
文摘To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition(K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bagQoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoSwords, Locality Constrained Feature Coding(LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines(SVM) clas-sifier. Our experimental results demonstrate the feasibility of the proposed classification method.
基金Supported by the National"863"Project(No.2014AA06A605)
文摘The transformation of basic functions is one of the most commonly used techniques for seismic denoising,which employs sparse representation of seismic data in the transform domain. The choice of transform base functions has an influence on denoising results. We propose a learning-type overcomplete dictionary based on the K-singular value decomposition( K-SVD) algorithm. To construct the dictionary and use it for random seismic noise attenuation,we replace fixed transform base functions with an overcomplete redundancy function library. Owing to the adaptability to data characteristics,the learning-type dictionary describes essential data characteristics much better than conventional denoising methods. The sparsest representation of signals is obtained by the learning and training of seismic data. By comparing the same seismic data obtained using the learning-type overcomplete dictionary based on K-SVD and the data obtained using other denoising methods,we find that the learning-type overcomplete dictionary based on the K-SVD algorithm represents the seismic data more sparsely,effectively suppressing the random noise and improving the signal-to-noise ratio.
基金supported in part by the National Laboratory of Radar Signal Processing Xidian Univrsity,Xi’an 710071,China。
文摘In distributed radar,most of existing radar networks operate in the tracking fusion mode which combines radar target tracks for a higher positioning accuracy.However,as the filtering covariance matrix indicating positioning accuracy often occupies many bits,the communication cost from local sensors to the fusion is not always sufficiently low for some wireless communication chan-nels.This paper studies how to compress data for distributed tracking fusion algorithms.Based on the K-singular value decomposition(K-SVD)algorithm,a sparse coding algorithm is presented to sparsely represent the filtering covariance matrix.Then the least square quantization(LSQ)algo-rithm is used to quantize the data according to the statistical characteristics of the sparse coeffi-cients.Quantized results are then coded with an arithmetic coding method which can further com-press data.Numerical results indicate that this tracking data compression algorithm drops the com-munication bandwidth to 4%at the cost of a 16%root mean squared error(RMSE)loss.
基金the National Natural Science Foundation of China(No.62001100)the Fundamental Research Funds for the Central Universities(No.2232019D3-52)Shanghai Sailing Program.(No.19YF1402000).
文摘Behind the prevalence of multimedia technology,digital copyright disputes are becoming increasingly serious.The digital watermarking prevention technique against the copyright infringement needs to be improved urgently.Among the proposed technologies,zero-watermarking has been favored recently.In order to improve the robustness of the zero-watermarking,a novel robust audio zerowatermarking method based on sparse representation is proposed.The proposed scheme is mainly based on the K-singular value decomposition(K-SVD)algorithm to construct an optimal over complete dictionary from the background audio signal.After that,the orthogonal matching pursuit(OMP)algorithm is used to calculate the sparse coefficient of the segmented test audio and generate the corresponding sparse coefficient matrix.Then,the mean value of absolute sparse coefficients in the sparse matrix of segmented speech is calculated and selected,and then comparing the mean absolute coefficient of segmented speech with the average value of the selected coefficients to realize the embedding of zero-watermarking.Experimental results show that the proposed audio zerowatermarking algorithm based on sparse representation performs effectively in resisting various common attacks.Compared with the baseline works,the proposed method has better robustness.
基金国家自然科学基金No.61701388+13 种基金教育部归国留学人员科研扶持项目No.K05055陕西省自然科学基础研究计划项目No.2016JM60792016年碑林区科技计划项目No.GX1605陕西省教育厅专项No.17JK0431The National Natural Science Foundation of China under Grant No.61701388the Scientific Research Foundation for the Returned Overseas Chinese ScholarsState Education Ministry of China under Grant No.K05055the Natural Science Basic Research Plan of Shaanxi Province under Grant No.2016JM6079the Science and Technology Project of Beilin District in 2016 under Grant No.GX1605the Special Item of Shaanxi Provincial Department of Education under Grant No.17JK0431
文摘壁画数字化修复工作极大降低了手工修复时带来的不可逆的风险。根据唐墓室壁画人工修复时先整体结构、后局部纹理的思路,提出一种基于形态学成分分析(morphological component analysis,MCA)分解的唐墓室壁画修复算法。首先结合唐墓室壁画的特点,采用改进的MCA方法进行图像分解,得到结构部分和纹理部分;然后根据图像分解后纹理和结构的复杂程度与稀疏程度,分别采用简化的全变分(total variation,TV)算法和K奇异值分解(K-singular value decomposition,K-SVD)算法进行修复。实验结果表明,该算法可兼顾纹理与结构的修复效果,唐墓室壁画中的裂缝现象的破损修复精度得到提高。
