基于行的二维离散小波变换的快速提升计算

二维离散小波变换计算量较大,并要求相当大的缓存空间,使JPEG2000在星载图像压缩和小型便携式设备中的应用受到限制。基于行的小波变换能降低对存储容量的要求,后拉伸变换的提升算法能减少离散小波变换的乘法运算量。为此,提出了一种基于行的快速提升算法,该算法将行列运算操作结合起来进行,以累进方式完成列向小波的提升步计算,节省了内存;并且充分利用了后拉伸变换的优点,减少了乘法运算量。     

提升的M通道小波变换在图像压缩中的应用

针对M通道小波变换对信号分频范围更细这一特点,利用基于空间域的小波提升方案,实现了M通道小波变换的提升分解。这种提升分解是不唯一的,可以利用这种分解的不唯一性,使变换具有所需的性质。为此给出了一种M通道快速提升小波变换的算法。该方法只要有分解算法,立即可以得到合成算法,而且将运算结果取为最接近的整数,就可实现整数到整数的小波变换。实验结果表明了该方法对图像压缩的有效性。     

一种新型分数阶小波变换及其应用

小波变换和分数Fourier变换是应用非常广泛的信号处理工具.但是,小波变换仅局限于时频域分析信号;分数Fourier变换虽突破了时频域局限能够在分数域分析信号,却无法表征信号局部特征.为此,提出了一种新型分数阶小波变换,该变换不但继承了小波变换多分辨分析的优点,而且具有分数Fourier变换分数域表征功能.与现有分数阶小波变换相比,新型分数阶小波变换可以实现对信号在时间-分数频域的多分辨分析.此外,该变换具有物理意义明确和计算复杂度低的优点,更有利于满足实际应用需求.最后,通过仿真实验验证了所提理论的有效性.     

A novel fractional wavelet transform and its applications

The wavelet transform (WT) and the fractional Fourier transform (FRFT) are powerful tools for many applications in the field of signal processing.However,the signal analysis capability of the former is limited in the time-frequency plane.Although the latter has overcome such limitation and can provide signal representations in the fractional domain,it fails in obtaining local structures of the signal.In this paper,a novel fractional wavelet transform (FRWT) is proposed in order to rectify the limitations of the WT and the FRFT.The proposed transform not only inherits the advantages of multiresolution analysis of the WT,but also has the capability of signal representations in the fractional domain which is similar to the FRFT.Compared with the existing FRWT,the novel FRWT can offer signal representations in the time-fractional-frequency plane.Besides,it has explicit physical interpretation,low computational complexity and usefulness for practical applications.The validity of the theoretical derivations is demonstrated via simulations.     

基于提升小波变换的信号降噪及其工程应用

为了解决炮膛检测中的噪声抑制问题,在讨论提升小波变换基本原理及其特点的基础上,采用提升小波变换的方法构造小波,介绍了基于插值细分法的提升小波,讨论了其降噪原理。通过实验对比研究了不同类型的经典提升小波和插值细分小波的降噪效果以及小波支集与降噪效果之间的关系,为选择降噪小波提供了理论依据。将该结论应用于炮膛检测系统中身管内径测量信号的降噪处理,取得了满意的效果。     

基于无抽样小波变换和MCE训练的纹理分类

提出了一种新的纹理分类的方法,该方法把基于无抽样小波变换的特征提取器和基于欧几里得距离的分类器进行了合并。把方差、偏态系数、峰态系数、三者的联合及谱直方图作为描述纹理图像不相重叠的图像窗的特征。一个使用线性转换矩阵的特征提取器对分类导向的特征做进一步的提取。利用基于欧几里得距离的分类器,每个纹理图像不相重叠的图像窗被确定到属于它的那一类。基于最小分类错误训练方法的特征提取器和分类器设计的合并使分类错误达到了最小化。使用该方法对25类BrodTex纹理图像进行了评估,分类精确度达到90%以上。     

基于提升小波变换的SPIHT编码算法研究

针对小波变换和多级树集合分裂图像编码具有浮点数运算、算法复杂等特点,采用整数实现的提升格式代替了原来的小波变换,并结合提升小波变换和DCT变换的特点,提出了一种改进方案。仿真结果表明,在相同的较低比特率情况下,与原算法相比,峰值信噪比有明显提高,取得了更优的压缩效果。     

Pansharpening of multispectral images using the nonseparable framelet lifting transform with high vanishing moments

This paper proposes a novel nonseparable lifting scheme for wavelet frames with high vanishing moments. A specific nonseparable framelet lifting transform (NFLT), combined with a modified covariance intersection (CI) algorithm, has been applied to pansharpening of multispectral images. Experiments are carried out on the multispectral and panchromatic images acquired by the SPOT, QuickBird and Landsat spaceborne sensors. Benefiting from the high order of vanishing moments, the proposed NFLT can distinguish the low- and high-frequency efficiently and can compact most of the energy into the low-pass subband. Thus the spectral distortion can be minimized. Experimental results show that the NFLT-CI method reduces the spectral distortion while improves the spatial resolution simultaneously, and outperforms the other state-of-the-art methods derived from various transforms and injection models.     

一种基于提升小波的图像融合方法

文章探讨了一种新的基于提升小波变换的图像融合算法。该算法能够有效地解决目前常用的多尺度分解方法所存在的运算速度慢、对内存的需求量大、不适于实时应用的局限性。同时，该算法可以最大限度地保留待融合图像的信息，提高了待融合图像的清晰度。实验结果表明，该算法在执行时间和融合后的图像质量上都优于传统的方法。     

基于提升整数小波变换的虹膜识别

提出了一种基于小波提升方案的虹膜识别算法。与传统基于卷积的小波变换方法相比,该方法在特征提取上计算简单,运算速度快,而且实现的是从整数到整数的变换,更有利于虹膜信息的量化。先对虹膜图像进行提升整数小波变换,然后对子图进行量化编码得到特征,采用屏蔽了噪声位的汉明距来进行模式识别。实验结果表明,基于提升整数小波变换的虹膜识别方法在识别率和等错率方面都能达到好的识别效果,在特征提取速度上也得到了较大提升,更能满足实时性的要求。     

自适应提升小波变换及飞机发动机故障分析

小波提升方案通过改变预测器和更新器构造出所需要的小波,这为机械设备故障特征分析中小波基函数的选择提供了方便。为了能在每个尺度上自适应选择与机械振动信号特征匹配的小波基函数,提出了一种更新器和预测器同时自适应地提升小波变换方法。在此方法中,采用先更新后预测的提升方案,分别通过信号的局部梯度大小和最小化预测误差来实现自适应更新和预测。将此方法应用在某飞机发动机故障分析中,实验结果表明,与经典小波变换相比该自适应提升小波变换分离的故障特征更明显效果更好。     

基于EMD和自适应提升小波分析的图像增强

针对经典和提升小波变换共同的缺陷,提出基于EMD和自适应提升小波分析的图像增强算法。对二维图像信息作EMD分解,提取出图像信息的IMF分量,对此IMF分量进行自适应提升小波分解并重构,得到增强图像。仿真及实验结果表明该方法具有有效性和实用性。     

快速提升小波变换及其在电位滴定信号处理中的应用

介绍了快速提升小波变换的原理和实现步骤,提出了一种基于提升算法的快速小波变换确定电位滴定终点新方法。分别对HAc、H2C2O4、H3PO4及苯酚的滴定曲线进行提升小波变换得到小波系数γNaOH,所得小波系数的极大值对应于滴定曲线的突跃部分。研究发现,滴定突跃越大,｜γ｜越大,酸性越弱,峰越宽。将由提升算法得到的计算结果与由实际加入的酸的量而求出在达到终点时理论上应消耗NaOH体积进行对照,验证了快速提升小波变换确定电位滴定终点的可靠性。对电位滴定实验数据的处理结果表明,该法快速、准确,可为计算机实时确定电位滴定终点开辟一条新的途径。     

提升小波变换及其在图像处理中的应用

提升算法是一种新的双正交小波构造方法,此方法大大降低了计算的复杂程度,因此该算法可以有效地减少程序运行时间.详细说明了提升算法的原理及实现步骤,并结合该算法介绍了它在图像处理中的一些应用.实验表明基于提升算法设计的图像处理系统有很好的性能.     

基于小波提升格式的织物疵点检测

为了满足织物疵点检测快速而准确的要求,提出了一种基于小波提升格式疵点检测的新方法。首先根据已知滤波器,通过提高消失矩阶次构造与织物纹理相匹配的小波。在此基础上,对构造小波的滤波器进行提升和对偶提升,来获得不同的提升算子和对偶提升算子,从而实现小波的提升分解。最后提取小波分解后的高频子图细节特征,通过与正常织物高频子图细节特征相比较,从而实现疵点检测。实验证明了该方法是可行有效的,检测准确率达到92.5%以上。     

Near infrared face recognition by combining Zernike moments and undecimated discrete wavelet transform

This study proposes a novel near infrared face recognition algorithm based on a combination of both local and global features. In this method local features are extracted from partitioned images by means of undecimated discrete wavelet transform (UDWT) and global features are extracted from the whole face image by means of Zernike moments (ZMs). Spectral regression discriminant analysis (SRDA) is then used to reduce the dimension of features. In order to make full use of global and local features and further improve the performance, a decision fusion technique is employed by using weighted sum rule. Experiments conducted on CASIA NIR database and PolyU-NIRFD database indicate that the proposed method has superior overall performance compared to some other methods in the presence of facial expressions, eyeglasses, head rotation, image noise and misalignments. Moreover its computational time is acceptable for on-line face recognition systems.     

基于提升小波变换与学习矢量量化网络的鉴别分析方法

提出了一种基于提升小波变换（LWT）和学习矢量量化网络（LVQ）相结合的鉴别分析方法。提升小波又叫作第二代小波,比传统的第一代小波变换更为快速有效,利用它的多分辨率特性,可以获取输入图像的低频信息并使图像降维。LVQ算法是在有教师状态下对竞争层进行训练的一种学习算法。LVQ网络结构简单,但却表现出比BP网络更强的有效性和鲁棒性。在ORL标准人脸库及现实人脸图像上的实验结果表明该方法具有很好的鉴别分析能力。     

Texture segmentation using wavelet transform

Texture analysis such as segmentation and classification plays a vital role in computer vision and pattern recognition and is widely applied to many areas such as industrial automation, bio-medical image processing and remote sensing. This paper describes a novel technique of feature extraction for characterization and segmentation of texture at multiple scales based on block by block comparison of wavelet co-occurrence features. The performance of this segmentation algorithm is superior to traditional single resolution techniques such as texture spectrum, co-occurrences, local linear transforms, etc. The results of the proposed algorithm are found to be satisfactory.     

Fall detection using single-tree complex wavelet transform

The goal of Ambient Assisted Living (AAL) research is to improve the quality of life of the elderly and handicapped people and help them maintain an independent lifestyle with the use of sensors, signal processing and telecommunications infrastructure. Unusual human activity detection such as fall detection has important applications. In this paper, a fall detection algorithm for a low cost AAL system using vibration and passive infrared (PIR) sensors is proposed. The single-tree complex wavelet transform (ST-C$\mathbb{C}$WT) is used for feature extraction from vibration sensor signal. The proposed feature extraction scheme is compared to discrete Fourier transform and mel-frequency cepstrum coefficients based feature extraction methods. Vibration signal features are classified into “fall” and “ordinary activity” classes using Euclidean distance, Mahalanobis distance, and support vector machine (SVM) classifiers, and they are compared to each other. The PIR sensor is used for the detection of a moving person in a region of interest. The proposed system works in real-time on a standard personal computer.