基于独立分量分析的图像融合算法

提出了一种基于独立分量分析(ICA)的图像融合算法。在图像配准的基础上，利用独立分量分析对图像进行训练获得独立分量基函数。对于待融合图像，通过训练得到的基函数对图像进行线性变换，然后在变换域根据不同的融合规则对图像进行融合，ICA反变换得到融合图像。仿真结果表明了该方法的有效性。 关键词：     

基于ICA的全色影像和多光谱影像融合算法

提出了一种新的基于独立成分分析的全色图像和多光谱图像的融合算法。对于两幅图像的重叠区域，使用独立成分分析去除遥感影像中高阶数据冗余问题，然后对通过独立成分分析所分离的各个分量进行叠加从而得到最终的融合结果。该方法的优点在于去除了原始图像上的数据冗余，提高了融合后图像的信息量和信噪比。     

基于曲波活性测度的SAR与多光谱图像融合

曲波变换是一种更适合于图像处理的多尺度几何分析方法,具有很强的方向性。结合活性测度将其应用于合成孔径雷达(SAR)图像和多光谱(MS)图像融合可以更好地表示图像中的有用特征。首先,对多光谱图像和SAR图像的R、G、B三波段分别进行曲波变换,粗尺度系数采用3×3窗口系数活性测度进行融合,细节尺度直接取大,对粗尺度和细节尺度系数重构后得到最终融合结果。采用熵、平均梯度、信噪比和扭曲程度对融合结果进行评价。实验结果表明,基于曲波活性测度的融合方法在保持MS光谱信息和提高空间分辨率上都有较好的结果。     

Remote sensing image fusion using the curvelet transform

This paper presents an image fusion method suitable for pan-sharpening of multispectral (MS) bands, based on nonseparable multiresolution analysis (MRA). The low-resolution MS bands are resampled to the fine scale of the panchromatic (Pan) image and sharpened by injecting highpass directional details extracted from the high-resolution Pan image by means of the curvelet transform (CT). CT is a nonseparable MRA, whose basis functions are directional edges with progressively increasing resolution. The advantage of CT with respect to conventional separable MRA, either decimated or not, is twofold. Firstly, directional detail coefficients matching image edges may be preliminarily soft-thresholded to achieve a noise reduction that is better than that obtained in the separable wavelet domain. Secondly, modeling of the relationships between high-resolution detail coefficients of the MS bands and of the Pan image is more fitting, being accomplished in the directional multiresolution domain. Experiments are carried out on very-high-resolution MS + Pan images acquired by the QuickBird and Ikonos satellite systems. Fusion simulations on spatially degraded data, whose original MS bands are available for reference, show that the proposed curvelet-based fusion method performs slightly better than the state-of-the art. Fusion tests at the full scale reveal that an accurate and reliable Pan-sharpening, little affected by local inaccuracies even in the presence of complex and detailed urban landscapes, is achieved by the proposed method.     

A method for condition monitoring and fault diagnosis in electromechanical system

Condition monitoring of electrical machines has received considerable attention in recent years. Many monitoring techniques have been proposed for electrical machine fault detection and localization. In this paper, the feasibility of using a nonlinear feature extraction method noted as Kernel independent component analysis (KICA) is studied and it is applied in self-organizing map to classify the faults of induction motor. In nonlinear feature extraction, we employed independent component analysis (ICA) procedure and adopted the kernel trick to nonlinearly map the Gaussian chirplet distributions into a feature space. First, the adaptive Gaussian chirplet distributions are mapped into an implicit feature space by the kernel trick, and then ICA is performed to extract nonlinear independent components of the Gaussian chirplet distributions. A thorough laboratory study shows that the diagnostic methods provide accurate diagnosis, high sensitivity with respect to faults, and good diagnostic resolution.     

独立成分相关分析的自适应故障监测方法

工业过程数据具有动态、非高斯等特性.独立成分分析(independent component analysis, ICA)既可以分析数据的非高斯形式,又可以极大地去除多变量间的耦合且满足独立性要求.本文引入粒子群算法优化ICA模型参数,自适应地确定独立成分个数.同时,提出一种基于隐马尔科夫链模型(hidden Markov model, HMM)的自适应检测限设计方法,将时间相关数据块的特征信息变化作为过程故障的检测依据.首先利用由时间窗方法确定的独立成分组成监测矩阵来训练HMM模型,旨在提高独立成分间相关性水平的表示能力;然后将得到的HMM模型对监测矩阵进行相关性评估,并在一定容许裕度的基础上设计评估值的自适应因子及检测限,并据此监测特征信息变化,动态地进行在线故障检测.最后, Tennessee Eastman (TE)仿真平台的实验结果表明了所提方法的有效性.     

Approach and applications of constrained ICA

This work presents the technique of constrained independent component analysis (cICA) and demonstrates two applications, less-complete ICA, and ICA with reference (ICA-R). The cICA is proposed as a general framework to incorporate additional requirements and prior information in the form of constraints into the ICA contrast function. The adaptive solutions using the Newton-like learning are proposed to solve the constrained optimization problem. The applications illustrate the versatility of the cICA by separating subspaces of independent components according to density types and extracting a set of desired sources when rough templates are available. The experiments using face images and functional MR images demonstrate the usage and efficacy of the cICA.     

基于独立分量分析和支持向量机的遥感影像融合分类算法

遥感影像分类是遥感定量化分析的重要手段,遥感影像融合是提高分类正确率的有效途径之一。本文提出一种遥感影像的融合分类算法。首先采用Contourlet变换对多光谱影像和全色影像进行融合,然后结合独立分量分析的去相关性、稀疏特性以及很好地捕捉影像重要边缘信息、纹理信息的能力,提取融合影像的独立分量特征,并用支持向量机实现分类。与其他算法的主、客观比较结果表明,该算法的实验效果较好,可有效地提高遥感影像的分类精度。     

一种盲源分离的优先进化自适应遗传算法

盲分离技术与独立分量分析(ICA)由于不需要知道信号的先验信息而得到广泛应用.ICA是信号处理的一种新技术.其基本目标是寻找线性变换矩阵,将观测的多维混合信号进行变换,变换后的输出信号各分量之间尽可能统计独立.将改进的遗传算法(GA)与ICA相结合,提出基于优先进化自适应GA的盲源分离算法,并与传统的遗传算法进行了比较,证实了其具有更好的收敛性和稳态性能.对3段声音信号进行了仿真,仿真结果证明了算法的有效性.     

ICA自适应核估计在多用户检测中的应用

将自适应核估计的ICA算法应用于DS-CDMA系统的多用户检测中,在没有关于混叠信号的分布假设时,根据信号的统计特性自动估计评价函数,采用自适应核密度估计ICA算法检测器的输出初始化独立分量分析的迭代,对任意混叠信号进行盲分离。通过与传统匹配滤波检测和ICA后处理匹配滤波检测进行仿真比对,证明了该算法在DS-CDMA多用户检测中的可行性和优越性。     

EMGdi signal enhancement based on ICA decomposition and wavelet transform

Diaphragmatic electromyogram (EMGdi) signal plays an important role in the diagnosis and analysis of respiratory diseases. However, EMGdi recordings are often contaminated by electrocardiographic (ECG) interference, which posing serious obstacle to traditional denoising approaches due to overlapped spectra of these signals. In this paper, a novel method based on wavelet transform and independent component analysis (ICA) is proposed to remove the ECG interference from noisy EMGdi signals. With the proposed method, the original independent components of contaminated EMGdi signal were first obtained with ICA. Then the ECG components contained were removed by a specially designed wavelet domain filter. After that, the purified independent components were reconstructed back to the original signal space by ICA to obtain clean EMGdi signals. Experimental results achieved on practical clinical data show that the proposed approach is better than several traditional methods include wavelet transform (WT), ICA, digital filter and adaptive filter in ECG interference removing.     

Novel infrared and visible image fusion method based on independent component analysis

The goal of infrared (IR) and visible image fusion is for the fused image to contain IR object features from the IR image and retain the visual details provided by the visible image. The disadvantage of traditional fusion method based on independent component analysis (ICA) is that the primary feature information that describes the IR objects and the secondary feature information in the IR image are fused into the fused image. Secondary feature information can depress the visual effect of the fused image. A novel ICA-based IR and visible image fusion scheme is proposed in this paper. ICA is employed to extract features from the infrared image, and then the primary and secondary features are distinguished by the kurtosis information of the ICA base coefficients. The secondary features of the IR image are discarded during fusion. The fused image is obtained by fusing primary features into the visible image. Experimental results show that the proposed method can provide better perception effect.     

Noninvasive fetal ECG estimation using adaptive comb filter

This paper describes a robust and simple algorithm for fetal electrocardiogram (FECG) estimation from abdominal signal using adaptive comb filter (ACF). The ACF can adjust itself to the temporal variations in fundamental frequency, which makes it qualified for the estimation of quasi-periodic component from physiologic signal, such as ECG. The validity and performance of the described method are confirmed through experiments on real fetal ECG data. A comparison with the well-known independent component analysis (ICA) method has also been presented.     

基于独立成分的局部建模方法及其在近红外光谱分析中的应用研究

建立了一种基于独立成分分析的局部建模新方法,该方法首先将独立成分分析(ICA)用于近红外光谱的特征提取,然后,根据所提取的独立成分选择校正集中与预测样本相邻近的样本构成校正子集,建立局部偏最小二乘(PLS)回归模型并对预测样本进行预测。将所提出的方法应用于烟草样品中尼古丁含量的测定,所得结果优于常用的全局建模方法。     

Kernel ICA: An alternative formulation and its application to face recognition

This paper formulates independent component analysis (ICA) in the kernel-inducing feature space and develops a two-phase kernel ICA algorithm: whitened kernel principal component analysis (KPCA) plus ICA. KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined by kernel. ICA seeks the projection directions in the KPCA whitened space, making the distribution of the projected data as non-gaussian as possible. The experiment using a subset of FERET database indicates that the proposed kernel ICA method significantly outperform ICA, PCA and KPCA in terms of the total recognition rate.     

A novel adaptive fast IHS transform fusion method driven by regional spectral characteristics for Gaofen-2 imagery

ABSTRACT

With the increasing diversity of applications based on the Gaofen-2 satellite imagery, broadly applicable methods to generate high quality fused images is a significant problem to investigate. To obtain an image with high spatial and spectral resolutions from given panchromatic (Pan) images and multispectral (MS) images, most existing fusion algorithms adopt a unified strategy for the whole image. However, regions have distinct characteristics that impact the spatial and spectral resolution processing, on account of their varying regional features. In this article, to satisfy the diverse needs of different regions, a novel fast IHS (Intensity-Hue-Saturation) transform fusion method driven by regional spectral characteristics is proposed to fuse Gaofen-2 imagery. First, by the fast IHS transform framework, the original intensity component is obtained from the upsampled MS imagery. Then, numerous independent regions of upsampled MS imagery are generated by a novel superpixel merging strategy, and the spectral characteristics of these regions are utilized for generating a fusion factor. Next, to acquire a new fused intensity component, the fusion factor is applied to guide the injection of details in the fusion procedure. This fusion factor adapts the method to meet the spatial and spectral resolution needs for each region. Finally, the difference between the new fused intensity component and the original one is regarded as the detail that needs to be injected; these are added equally to the different bands of the upsampled MS imagery to yield the final fused multispectral image. In comparison with other classical algorithms, the visual and statistical analysis reveal that our proposed method can provide better results in improving spatial detail and preserving spectral information.     

基于PCA和数据融合的遥感图像数字水印算法

提出了一种向遥感图像中嵌入水印以保护其版权的算法。算法将数据融合技术和数字水印技术相结合,首先将全色图像进行小波分解,提取图像分解后的第三级低频边缘特征,利用PCA变换得到边缘特征的第一主分量作为水印信息,将水印与第三级中频进行融合;然后进行小波逆变换得到重构图像;最后采用小波变换和PCA融合法将含有水印的全色图像和多光谱图像相融合。提取水印时使用独立分量分析(ICA)方法。实验表明,该算法可以保护遥感图像的版权和进行真伪认证,且不破坏原始遥感图像的信息和特征,是有效可行的。     

应用复小波和独立成分分析的人脸识别

结合双树复小波变换（DT-CWT）和独立成分分析（ICA）提出了一种人脸识别新方法。该方法首先应用双树复小波变换提取图像的特征向量,接着通过主成分分析（PCA）降低特征向量的维数,在此基础上应用独立成分分析提取统计上独立的特征向量,然后基于相关系数的分类器对特征向量进行分类。双树复小波变换具有方向与尺度选择性,并能有效的保持图像的频域信息,其与独立成分分析相结合提取的特征具有良好的分类性能。在ORL和AR人脸图像数据库上进行算法验证的结果表明该方法的有效性。     

基于改进的独立分量分析的人脸识别方法

将独立分量分析（Independent Component Analysis，ICA）作为人脸特征提取方法。ICA所提取的特征分类能力强、相互独立，对像素间高阶统计特性敏感，并且不易受光照变化的影响。实验结果表明，基于IcA的人脸特征提取方法的识别性能优于特征脸法。针对传统的ICA算法（Informax算法）存在迭代次数多，难收敛，并且需要人工设定步长来调整学习速度的不足，本文采用FastICA作为ICA的快速算法，并将其关键迭代步骤加以改进，减少了耗时的雅可比矩阵求逆的运算次数。所提出的改进的FastICA具有无需人工参与，收敛速度快，迭代次数少的优点。在特征选择方面，本文将遗传算法（Genetie Algorithm，GA）应用到独立分量的选择与优化中，从而在保证较高识别性能的前提下，获得最优的人脸特征子集。