运用离散二进小波变换分离声目标的信号与噪声

本文介绍离散二进小波变换的原理和实现多尺度离散二进小波变换的MALLAT塔式算法,给出了一个利用多尺度离散二进小波变换对战术声目标进行信号与噪声分离的实例;所得计算机模拟结果表明,运用离散二进小波变换滤除噪声,能够获得更高的信噪比。     

一种基于离散小波变换的图像数字水印算法

提出了一种以离散小波变换为基础的图像数字水印算法。详细介绍了离散小波变换和基于离散小波变换的数字水印嵌入算法及检测算法。     

基于平稳小波的X射线底片数字图像去噪方法研究

本文介绍了小波变换的基本内容,重点介绍了离散小波变换和平稳小波变换,并采用平稳小波变换对X光底片进行处理,将平稳小波与离散正交小波进行了对比实验。实验结果证明,利用平稳小波去噪具有较好的视觉效果,提高了被检对象缺陷的辨识率。     

提升小波的FPGA实现

小波变换在图像压缩领域正在取代离散余弦变换.提升小波是离散小波变换的一种实现方案,它具有运算量小、可原位计算的特点.本文在FPGA中实现了一级二维提升小波变换,弥补采用DSP实现小波变换对存储空间的需求.仿真表明其所耗资源较少,其硬件结果与MATLAB软件结果基本一致.     

基于离散正交小波变换的图象去噪方法

给出了一种基于离散正交小波变换的图象去噪方法,该方法通过二维离散小波变换将图象投影到小波变换域,通过对小波变换系数进行阈值处理实现二维图象的去噪,并用实验验证了该方法的有效性。     

视频运动图像中人脸清晰度的分析与捕捉

图像的清晰度主要取决于它的高频分量的能量,离散快速傅里叶变换和小波变换,是两种优秀的频域变换工具。介绍了利用离散快速傅里叶变换和小波变换分析运动图像中人脸清晰度的方法：首先利用直方图均衡化以及形态学操作对图像进行预处理;然后进行人脸检测和人脸跟踪;最后进行人脸的捕捉。对比了离散快速傅里叶变换和几种小波变换的质量和效率,实验证明基于bior基的小波变换是最有效的。     

一种新的彩色图像数字水印算法

数字水印是有关多媒体信息内容安全的一种技术.本文应用离散脊波变换(Discrete Ridgelet Transform),设计并实现了一种基于离散眷波变换和离散小波变换的图像水印算法,算法分别在彩色图像的离散小波变换域和离散眷波变换域嵌入彩色图像水印,可以抵抗噪声、JPEG压缩、改变亮度、改变对比度、改变颜色、lens blur、缩放、剪切和一些联合攻击.     

基于小波变换的医学图像压缩技术

分析了医学图像压缩的必要性,简要介绍了二维离散小波变换和Mallat算法,在此基础上探讨了基于小波变换的医学图像压缩技术。实验结果表明,小波变换算法具有较高的压缩比和较好的图像恢复质量。     

离散小波变换算法剖析及其通用程序实现

针对小波变换工程应用软件开发需要,结合Mallat算法原理分析介绍了离散小波变换的主要功能步骤以及程序设计技术的关键问题.算法采用Delphi语言实现,大量数据验算表明,程序运算中间及最终结果和用Matlab小波工具箱编写的小波变换程序执行情况完全一致.主要算法均以子函数形式给出,便于研究人员把在Matlab中开发的小波变换应用算法成果转化为其它高级语言程序,构建独立的专用软件系统.     

基于DSP离散小波变换的信号不连续性检测技术

本文介绍了离散小波变换的原理及其在处理非平稳信号时的优势.通过使用离散小波变换对输入的带有间断点的信号进行分析,给出了经过小波变换的结果的图形并进行了分析.本实验硬件平台使用TMS320C5509APGE开发板.     

改进的Morlet小波及其变换

提出了一个改进的Morlet小波,并在此基础上给出了Morlet小波变换的完全重构公式,这个重构公式不需要Morlet小波满足小波容许条件,使得Morlet小波变换在理论上趋于完善。改进后的Morlet小波其尺度参数替换为小波主频参数,参数有明确的物理意义,用它作为核函数的小波变换把时间信号映射到时间-频率域。重构公式的提出可拓宽Morlet小波的应用范围,引进了一个由Morlet小波变换及其逆变换构建的时-频滤波器并将其用于地震信号处理以提高其分辨率。从理论上分析了Morlet小波变换与S变换的区别,并用实际算例验证了分析结果。     

小波投影算子在工业图像边缘检测及抗噪分析中的应用

采用基于小波变换投影算子的快速多尺度边缘检测迭代算法，计算表明，迭代次数取5－9，输出图像信噪比较佳；并与传统的Marr边缘检测方法作了实验比较分析，小波变换方法针对工业现场采集的监视目标图像污染严重及噪声干扰等实际情况是一种较好的方法，在边缘定位精度，图像细节丢失等方面优于Marr方法，对噪声干扰也有较好的鲁棒性。     

储罐底板的漏磁检测信号处理中小波奇异性检测理论的应用

漏磁检测是无损检测中的一种重要方式,采用漏磁法采集到的漏磁信号具有数据量大和附带有大量的噪声的特性,其中,缺陷通常表现为输出信号发生突变,因此考察信号突变点的变化对漏磁检测有很重要的意义。本文利用了小波分析其独特的时域和频域特性,分析了基于小波变换的漏磁信号奇异点的定位方法和奇异性程度的计算方法,对漏磁信号进行处理,使信号便于存储和分析,仿真结果表明该方法是有效的。     

Basic processes of Chinese character based on cubic B-splinewavelet transform

A novel approach based on cubic B-spline wavelet transform is proposed to process Chinese character including character compression, type zooming-in, and typeface composition. The basic idea is that a Chinese character is described by its contours which are represented by cubic B-spline functions, and each contour is decomposed into the details or the control points (wavelet coefficients) at different resolution levels. For character compression, there are two methods, one directly treats the details of wavelet coefficients and the other considers the sub-curves piecing together at the different resolution levels. In the type zooming-in, the wavelet reconstruction is used to scale the Chinese character with arbitrary size and the wavelet filter is used to improve the quality of the enlarged type. For typeface composition, the new style typefaces of Chinese character are obtained by editing and modifying the details at different resolution levels. The concrete algorithms are also given as well as the experimental results     

基于小波分析的图像子带增强算法研究

介绍了图像的小波分解与重构,通过理论分析和实验研究了基于小波分析的图像子带增强算法,重点是小波基的选取以及图像分解层次对图像中目标边缘增强的影响研究,并针对目标边缘突出,将其与传统图像增强方法进行了比较。实验表明,针对目标边缘突出,基于小波分析的图像子带增强算法优于传统的图像增强方法,在该算法中,小波基选取db1-db5较好,分解层次定为5层增强效果优。     

Comparison and improvement of wavelet‐based image fusion

The wavelets used in image fusion can be categorized into three general classes: orthogonal, biorthogonal, and non‐orthogonal. Although these wavelets share some common properties, each wavelet also has a unique image decomposition and reconstruction characteristic that leads to different fusion results. This paper focuses on the comparison of the image‐fusion methods that utilize the wavelet of the above three general classes, and theoretically analyses the factors that lead to different fusion results. Normally, when a wavelet transformation alone is used for image fusion, the fusion result is not good. However, if a wavelet transform and a traditional fusion method, such as an IHS transform or a PCA transform, are integrated, better fusion results may be achieved. Therefore, this paper also discusses methods to improve wavelet‐based fusion by integrating an IHS or a PCA transform. As the substitution in the IHS transform or the PCA transform is limited to only one component, the integration of the wavelet transform with the IHS or PCA to improve or modify the component, and the use of IHS or PCA transform to fuse the image, can make the fusion process simpler and faster. This integration can also better preserve colour information. IKONOS and QuickBird image data are used to evaluate the seven kinds of wavelet fusion methods (orthogonal wavelet fusion with decimation, orthogonal wavelet fusion without decimation, biorthogonal wavelet fusion with decimation, biorthogonal wavelet fusion without decimation, wavelet fusion based on the ‘à trous’, wavelet and IHS transformation integration, and wavelet and PCA transformation integration). The fusion results are compared graphically, visually, and statistically, and show that wavelet‐integrated methods can improve the fusion result, reduce the ringing or aliasing effects to some extent, and make the whole image smoother. Comparisons of the final results also show that the final result is affected by the type of wavelets (orthogonal, biorthogonal, and non‐orthogonal), decimation or undecimation, and wavelet‐decomposition levels.     

图像平滑的2维小波插值方法

在应用扩散方程进行图像平滑时,常规的方法是对扩散方程差分化构造差分方程,利用初边值条件求解。这种方法误差传播快,精度不高。因此,构造了2维小波插值函数,利用它来求解扩散方程,并分析得到用小波插值函数求解Alvarez模型的方法。由于小波函数具有良好的局部性,求解扩散方程比用差分方法求解具有精度高,误差传播速度慢,对时间步长不敏感等优点。在数值实验中,给出了本文方法的有效性及相对于差分方法求解的优点。     

基于局部小波矩的图像匹配算法

该文将图像的视觉不变矩特征引入到图像匹配领域中,通过提取图像的局部小波矩,提出了一种基于局部小波矩的图像匹配算法。并分别对所提出的算法,基于一般不变矩特征的匹配算法有关实验数据进行了对比分析,结果表明了该文所提出算法的可行性和有效性。     

A new implementation of discrete bionic wavelet transform: Adaptive tiling

Bionic wavelet transform (BWT) is a biomodel-based adaptive time–frequency analysis technique. Due to its nonlinearity, it is difficult to realize the inverse BWT. To solve this problem, this paper introduces a new implementation for the discrete BWT (DBWT). The T-function from BWT is used to split the dyadic tiling map of DWT to obtain an adaptive DBWT tiling of the time–frequency plane. Quadrature-mirror filters, organized as the DBWT tiling map, are then employed to decompose the signal. This DBWT implementation makes the distortionless signal reconstruction possible. DBWT was used to decompose both simulated signal and actual nonstationary signals. Results show that DBWT performs better than discrete wavelet transform in demonstrating a more concentrated coefficient distribution in time–frequency plane. This proposed DBWT implementation will make BWT more applicable for the future nonstationary signal analysis.