Promote the Compression Efficiency of Digital Images by Using Improved CUR Matrix Decomposition Algorithm

In order to overcome the problem that the CUR matrix decomposition algorithm loses a large amount of information when compressing images, the quality of reconstructed images is not high, we propose a CUR matrix decomposition algorithm based on standard deviation sampling. Because of retaining more image information, the reconstructed image quality is higher under the same compression ratio. At the same time, in order to further reduce the amount of image information lost during the sampling process of the CUR matrix decomposition algorithm, we propose the SVD-CUR algorithm. The experimental results verify that our algorithm can achieve high image compression efficiency, and also demonstrate the high precision and robustness of CUR matrix decomposition algorithm in dealing with low rank sparse matrix data.

DWT-SVD Based Image Steganography Using Threshold Value Encryption Method

Digital image steganography technique based on hiding the secret data behind of cover image in such a way that it is not detected by the human visual system.This paper presents an image scrambling method that is very useful for grayscale secret images.In this method,the secret image decomposes in three parts based on the pixel’s threshold value.The division of the color image into three parts is very easy based on the color channel but in the grayscale image,it is difficult to implement.The proposed image scrambling method is implemented in image steganography using discrete wavelet transform(DWT),singular value decomposition(SVD),and sorting function.There is no visual difference between the stego image and the cover image.The extracted secret image is also similar to the original secret image.The proposed algorithm outcome is compared with the existed image steganography techniques.The comparative results show the strength of the proposed technique.

A Color Image Encryption Scheme Based on Singular Values and Chaos

The security of digital images transmitted via the Internet or other public media is of the utmost importance.Image encryption is a method of keeping an image secure while it travels across a non-secure communication medium where it could be intercepted by unauthorized entities.This study provides an approach to color image encryption that could find practical use in various contexts.The proposed method,which combines four chaotic systems,employs singular value decomposition and a chaotic sequence,making it both secure and compression-friendly.The unified average change intensity,the number of pixels’change rate,information entropy analysis,correlation coefficient analysis,compression friendliness,and security against brute force,statistical analysis and differential attacks are all used to evaluate the algorithm’s performance.Following a thorough investigation of the experimental data,it is concluded that the proposed image encryption approach is secure against a wide range of attacks and provides superior compression friendliness when compared to chaos-based alternatives.

基于DDCT与TCSVD的人脸特征提取与识别算法

提出一种基于分块离散余弦变换(DCT)与奇异值分解阈值压缩(TCSVD)的人脸特征提取与识别算法。该算法对人脸图像进行分块DCT变换,根据图像块位置和能量分布选择不同的DCT高低频分量构建特征矩阵,通过对每个图像块的特征矩阵进行SVD阈值压缩和特征组合来构建人脸鉴别特征,并利用分类器进行特征分类与识别。AR人脸库上的实验结果验证了该算法的有效性。

Angle estimation for bistatic MIMO radar with unknown mutual coupling based on three-way compressive sensing

The problem of angle estimation for bistatic multiple-input multiple-output radar in the present of unknown mutual coupling (MC) is investigated, and a three-way compressive sensing (TWCS) estimation algorithm is developed. To exploit the inherent multi-dimensional structure of received data, a trilinear tensor model is firstly formulated. Then the de-coupling operation is followed. Thereafter, the high-order singular value decomposition is applied to compress the high dimensional tensor to a much smaller one. The estimation of the compressed direction matrices are linked to the compressed trilinear model, and finally two over-complete dictionaries are constructed for angle estimation. Also, Cramer-Rao bounds for angle and MC estimation are derived. The proposed TWCS algorithm is effective from the perspective of estimation accuracy as well as the computational complexity, and it can achieve automatically paired angle estimation. Simulation results show that the proposed method has much better estimation accuracy than the existing algorithms in the low signal-to-noise ratio scenario, and its estimation performance is very close to the parallel factor analysis (PARAFAC) algorithm at the high SNR regions. © 2017 Beijing Institute of Aerospace Information.

A Robust and Efficient Compressed Sensing Algorithm for Wideband Acoustic Imaging

Wideband acoustic imaging,which combines compressed sensing(CS)and microphone arrays,is widely used for locating acoustic sources.However,the location results of this method are unstable,and the computational efficiency is low.In this work,in order to improve the robustness and reduce the computational cost,a DCS-SOMP-SVD compressed sensing method,which combines the distributed compressed sensing using simultaneously orthogonal matching pursuit(DCS-SOMP)and singular value decomposition(SVD)is proposed.The performance of the DCS-SOMP-SVD is studied through both simulation and experiment.In the simulation,the locating results of the DCS-SOMP-SVD method are compared with the wideband BP method and the DCS-SOMP method.In terms of computational efficiency,the proposed method is as efficient as the DCS-SOMP method and more efficient than the wideband BP method.In terms of locating accuracy,the proposed method can still locate all sources when the signal to noise ratio(SNR)is−20 dB,while the wideband BP method and the DCS-SOMP method can only locate all sources when the SNR is higher than 0 dB.The performance of the proposed method can be improved by expanding the frequency range.Moreover,there is no extra source in the maps of the proposed method,even though the target sparsity is overestimated.Finally,a gas leak experiment is conducted to verify the feasibility of the DCS-SOMP-SVD method in the practical engineering environment.The experimental results show that the proposed method can locate both two leak sources in different frequency ranges.This research proposes a DCS-SOMP-SVD method which has sufficient robustness and low computational cost for wideband acoustic imaging.

基于压缩感知的缺失机械振动信号重构新方法

针对工业机械设备实时监测中不可控因素导致的振动信号数据缺失问题,提出一种基于自适应二次临近项交替方向乘子算法(adaptive quadratic proximity-alternating direction method of multipliers, AQ-ADMM)的压缩感知缺失信号重构方法。AQ-ADMM算法在经典交替方向乘子算法算法迭代过程中添加二次临近项,且能够自适应选取惩罚参数。首先在数据中心建立信号参考数据库用于构造初始字典,然后将K-奇异值分解(K-singular value decomposition, K-SVD)字典学习算法和AQ-ADMM算法结合重构缺失信号。对仿真信号和两种真实轴承信号数据集添加高斯白噪声后作为样本,试验结果表明当信号压缩率在50%~70%时,所提方法性能指标明显优于其它传统方法,在重构信号的同时实现了对含缺失数据机械振动信号的快速精确修复。

基于SVD-Schmidt正交化的压缩感知测量矩阵的优化

压缩感知(CS)理论中测量矩阵的性能优劣直接影响信号重构性能。为了优化测量矩阵提高其重构性能,本文提出了一种基于奇异值分解-施密特(SVD-Schmidt)正交化的CS测量矩阵优化方法。首先对测量矩阵进行奇异值分解(SVD)并选择最大的奇异值替换原来的奇异值形成新的矩阵,同时对其进行施密特正交化,对矩阵的列进行单位化,通过行和列不断循环交替自适应迭代优化得到优化后的测量矩阵。通过一维信号和二维图像的仿真实验验证所提方法的优越性。一方面,本文方法优化的测量矩阵互相关性明显降低;另一方面,实验仿真结果证明了测量矩阵经过优化之后提高了信号重构性能,本文方法重构性能优于现有的SVD法和特征值分解法。

强噪声中检测微弱目标信号特征的量子信号处理算法

随着噪声功率的增强,微弱目标信号的特征受噪声污染变得模糊且难以区分,导致微弱信号检测算法失效,提出一种可以保护目标信号特征的量子信号处理方法——局域半经典信号分析算法。详细介绍了算法实现量子化的原理和在量子域中保护目标信号特征的性质;给出算法步骤以及重要参数的计算方式;将所提算法与奇异值分解、小波阈值降噪算法结合进行了仿真分析和实验验证。结果表明,所提算法保护目标信号特征的能力可以帮助降噪算法检测极低信噪比的微弱信号,与其他方法结合可极大改善信噪比,准确提取信噪比为-30 dB的微弱目标信号,算法性能优越。

高维矩阵奇异值分解的快速计算方法对比分析及应用

为在大数据环境下处理高维矩阵和应用奇异值分解提供更高效的解决方案,从而加速数据分析和处理速度,通过研究随机投影以及Krylov子空间投影理论下关于高维矩阵求解特征值特征向量(奇异值奇异向量)问题,分别总结了6种高效计算方法并对其相关应用研究进行对比分析。结果表明,在谱聚类的应用上,通过降低核心步骤SVD(Singular Value Decomposition)的复杂度,使优化后的算法与原始谱聚类算法的精度相近,但大大缩短了运行时间,在1200维的数据下计算速度相较原算法快了10倍以上。同时,该方法应用于图像压缩领域,能有效地提高原有算法的运行效率,在精度不变的情况下,运行效率得到了1~5倍的提升。

基于深度神经网络的存储压缩方法研究

由于深度神经网络存在内存密集和计算密集两大特征,其无法满足移动设备存储的需求。为此,提出一种基于深度神经网络的存储压缩方法,该方法通过SVD矩阵奇异值分解方法,并辅以网络剪枝技术,在深度神经网络运行过程中引入少量神经元,并设定神经元数连接数,降低权重规模。通过一系列方法对策,尽可能在不损失精度的前提下,对深度神经网络模型大幅度压缩,其压缩程度达到了5×、15×。

奇异值分解带通滤波背景抑制和去噪

针对可见光图像弱小目标检测中的背景抑制和去噪问题,提出了奇异值分解(Singular Value Decomposition,SVD)带通滤波新方法.首先分析了图像奇异值与目标、噪声和图像背景的关系,结果表明奇异值的高序部分更多地反映图像噪声,中序部分更多地反映目标性质,而低序部分更多地反映图像背景.以此为依据提出了SVD-Ⅰ型和SVD-Ⅱ型两种带通滤波器,并给出了奇异值曲线转折点法和门限准则法两种滤波器参数确定方法.实验表明SVD带通滤波能有效抑制图像背景,去除噪声,进而提高弱小目标的信噪比.

基于稀疏分解的振动信号数据压缩算法

针对齿轮传动装置在状态监测与故障诊断过程中面临的大量振动信号传输困难问题,提出利用K-SVD算法进行信号的稀疏分解,进而完成对大量振动数据的压缩。传统K-SVD算法在字典更新过程中对时间的消耗量较大,特别是在大量振动数据压缩过程中,对数据压缩效率较低,为此提出一种K-SVD字典更新的改进算法。改进算法从单次迭代过程中参与更新的字典原子列数出发,每次奇异值分解后对多列字典原子同时进行赋值,从而减少单次迭代计算量。根据不同原子列数在稀疏分解过程中的迭代收敛次数、时间消耗与重构峰值信噪比,以此确定最佳的字典更新列数。实验结果表明:传统K-SVD算法对振动信号的数据压缩效率较低,改进算法能够在保证信号压缩比与重构效果的前提下,有效缩短训练字典的时间消耗。

非均匀噪声分布心电信号的奇异值小波消噪法

针对一般消噪法对噪声非均匀分布心电信号消噪存在的不足,提出基于奇异值分解和小波阈值消噪相结合的消噪方法。该方法利用矩阵的奇异值分解将噪声非均匀分布的心电信号正交分解为噪声分布相对均匀的分量,在正交子空间中对每个分量进行小波阈值消噪,重构消噪后分量,得到消噪后的心电信号。研究结果表明:本方法有效地克服了因噪声分布不均匀而造成的小波阈值选择矛盾的缺点,有效地消除了大噪声区域的噪声,又完好保存小噪声区域的心电特征信息,且消噪后的信号与无噪信号之间的欧氏距离最小。

基于SVD的无线多媒体传感器网络图像压缩机制

针对无线多媒体传感器网络图像压缩问题,从节点协作特性出发,提出了一种基于奇异值分解的图像压缩传输方案.首先研究了基于SVD的分块图像自适应压缩算法,以平衡网络能耗为目标,将相机节点和普通节点按照角色分工,协作完成图像采集、压缩和传输工作.相机节点负责采集图像信息,然后将图像分块发送给簇内普通节点;簇内普通节点对分块图像进行自适应压缩并将数据发送给簇头节点;簇头节点再将压缩后的数据转发至基站.仿真结果表明,提出的网络拓扑方案和图像压缩传输机制极大地缓解了图像采集节点的能耗压力,有效地平衡了网络的节点能耗.与JPEG2000协同图像压缩方案相比,基于SVD的图像处理方案的网络总能耗更少;且后者能够有效地对图像进行压缩传输,平衡网络能耗,延长整个网络的生命周期.

基于奇异值分解的压缩感知定位算法

为了使观测字典满足约束等距性条件,保证算法的定位精度,提出一种基于奇异值分解的压缩感知定位算法。新算法首先将感知区域网格化,把定位问题转化为压缩感知问题,然后利用奇异值分解原理对观测字典进行分解,得到的新的观测字典有效地满足了约束等距性条件,且对观测值的预处理过程不影响原信号的稀疏性,从而有效地保证算法的重建性能,提升定位精度。仿真实验结果表明:相比于基于Orth的稀疏目标定位算法,基于SVD的压缩感知定位算法的定位性能更优,抗噪性、适应性更强,且算法复杂度低。

半张量积低存储压缩感知方法研究

由于随机观测矩阵的随机性,存在数据存储量大、内存占用率高、数据计算量大以及难以面向大规模实际应用等问题.为此,提出了一种可有效降低随机观测矩阵所占存储空间的半张量积压缩感知(STP-CS)方法.利用该方法,构建低维随机观测矩阵,经奇异值分解(SVD)优化后对原始信号进行采样,并利用拟合0-范数的迭代重加权方法进行重构.实验利用2维灰度图像进行测试,并对重构图像的峰值信噪比,结构相似度等指标进行了统计和比较.实验结果表明,本文所述的STP-CS方法在不改变随机观测矩阵数据类型的前提下,可将观测矩阵减小至传统CS模型中观测矩阵所占内存空间的1/256(甚至更低),同时仍保持很高的重构质量.

基于DWT-SVD压缩量化的数字图像盲水印算法

为了解决奇异值分解(singular value decomposition,SVD)算法提取水印时需要原始载体图像的缺陷,以及量化索引调制(quantization index modulation,QIM)均匀量化不适用于非均匀信号的问题,通过引入μ律压缩技术,提出一种新的基于DWT-SVD压缩量化的数字图像盲水印算法。该算法对载体图像进行分块,对每一分块实施离散小波变换(discrete wavelet transform,DWT),以及对变换后的近似部分系数进行SVD分解,使用μ律压缩函数压缩分解后的最大奇异值,用QIM的方法嵌入二值水印。算法只用到了最大奇异值,可以盲提取水印,消除因传输原始载体图像产生的不安全性,μ律压缩技术也减小了嵌入水印对原始载体图像的扰乱。仿真实验结果表明,该算法保持了较高的透明性,并对高斯噪声、中值滤波、联合图像专家小组(joint photographic experts group,JPEG)压缩、缩放等常见攻击具有更强的鲁棒性。

奇异值分解对连续Morlet小波变换的压缩和提纯

研究连续Morlet小波变换矩阵行矢量的线性相关性,这种相关性使得连续Morlet小波变换的结果存在很大的冗余,利用奇异值分解(Singular value decomposition,SVD)来压缩这种冗余性。理论分析表明,SVD技术可以将连续Morlet小波变换矩阵的信息完全压缩到少数的非零奇异值及其对应的正交奇异矢量中,分析压缩前后数据量的比例,证实矩阵维数越大,压缩效果越好。研究确定性信号和噪声的连续Morlet小波变换结果的奇异值的分布特点,发现确定性信号的有效奇异值数量由信号中的频率数量决定,有效奇异值之后的奇异值会很快地下降到零,而噪声的奇异值序列的变化比较均匀,下降速度比较缓慢。利用确定性信号和噪声奇异值的这种差异,可以实现对含噪信号的连续Morlet小波变换结果的提纯,只要选择前面合适的奇异值进行SVD重构,大部分噪声奇异值的信息会被抛弃,因而可在很大程度上消除噪声对连续Morlet小波变换结果的影响。

基于奇异值分解(SVD)的图像压缩

根据奇异值分解的基本原理及其特点,给出了运用奇异值分解进行图像压缩的方法.通过简单的例子说明了该方法进行图像压缩的基本过程,给出了压缩流程.并通过MAT-LAB编程对实际图像进行处理,表明了该方法的有效性.