遥感图像小波变换系数的统计分布

本文研究了遥感图像经张量积小波与非张量积多元小波变换后得到的小波系数的统计分布及其特性,得出了遥感图像经双正交9-7整数小波变换后的系数的每个高频子带在能量分布上近似关于原点对称,每个高上频子带都具有“非高斯”性,每层的三个高频子带分布相似,第一层的各子带的值在零点附近更为集中,所以在零点形成更陡更窄的“尖峰”的结论.     

多小波子空间上的单小波表示

本文在较弱的条件下,建立了2重多小波子空间与单小波子空间的关系.即由2重多小波构造出单小波.一方面,这种单小波的平移伸缩与2重多小波的平移伸缩生成的子空间是完全相同的;另一方面,它具有插值性.因此通过构造出的单小波建立了多小波子空间上的Shannon型采样定理.     

M带含参数对称正交插值尺度函数的构造

<正>1引言基于多分辨分析方法,已经构造了大量各种各样的单小波,如紧支撑的正交的Daubechies小波,紧支撑半正交的样条小波等.Daubechies证明了除Haar小波外不存在紧支撑正交对称的2带单小波.为了弥补2带单小波的不足,许多数学工作者将它进行推广得到了:双正交小波、多小波、多带小波等几大分支.而在实际应用中有时需要处理一些具有相对窄的带宽的高频信号,所以很有必要研究多带小波.同时多带小波能同时拥     

用于JPEG2000图像认证的鲁棒性水印算法

针对JPEG2000图像压缩标准所具有的渐进传输、一次编码多次解码等特性,提出了一种基于图像特征的鲁棒性图像认证算法.该算法在JPEG2000编码过程中,先根据图像不变特征,生成认证水印,再根据实际的鲁棒性认证需求,在量化后的小波系数中确定每个子带的认证水印嵌入位平面,最后基于小波系数位平面的特征嵌入认证水印.算法不仅能适应JPEG2000各种灵活的编码方式,还能定位图像篡改的位置.实验结果验证了图像认证算法对可允许图像操作的鲁棒性以及对图像篡改的敏感性.     

基于多尺度几何分解的光照不均图像增强方法

针对光照不均匀的图像,结合W系统和NSCT变换,提出了一种新的图像增强方法.方法首先利用W变换对图像进行多尺度分解,然后利用NSCT中的非下采样方向滤波器组对尺度分解中的高频部分进行方向分解,得到不同尺度不同方向上的变换系数.在多尺度几何分解的基础上,对低频子带图像采用动态直方图均衡化、高频子带图像采用同态滤波的方法进行增强处理,最后利用非线性函数减小图像明、暗部分灰度值的差异,得到最后的增强结果.仿真实验结果表明,算法无论在视觉效果上还是客观评价指标上都优于其他被比较的四种增强算法,对于过亮、过暗以及局部光照不均匀的图像均取得了更好的增强效果,在增强图像细节的同时能有效抑制图像的伪吉布斯失真和过增强失真.在评价指标上,算法对三组经典图像处理后的增强图像的信息熵分别达到了10.0755、9.7879、10.5338,明显优于其他方法.     

芬兰淡水湖冰下溶解氧浓度变化规律的离散小波分析北大核心

以离散小波变换的多尺度分析理论为依据,用Daubechies系列小波对芬兰Valkea-kotinen淡水湖第二测点自2011年1月13日至5月17日不同深度溶解氧浓度的采集数据进行分解与重构.通过db1-db6小波分解效果比较,发现db4小波的重构效果较好.采用db4小波对该位置各深度溶解氧浓度进行多尺度分析,得到数据的低频和高频重构曲线,分析曲线的变化规律.最后,利用离散小波变换尺度为2的幂次这一特点,给出有利于数据分析的测量时间间隔.     

芬兰淡水湖冰下溶解氧浓度变化规律的离散小波分析

以离散小波变换的多尺度分析理论为依据,用Daubechies系列小波对芬兰Valkea-kotinen淡水湖第二测点自2011年1月13日至5月17日不同深度溶解氧浓度的采集数据进行分解与重构.通过db1-db6小波分解效果比较,发现db4小波的重构效果较好.采用db4小波对该位置各深度溶解氧浓度进行多尺度分析,得到数据的低频和高频重构曲线,分析曲线的变化规律.最后,利用离散小波变换尺度为2的幂次这一特点,给出有利于数据分析的测量时间间隔.     

基于小波与齐次Besov空间的图像分割算法

从具有全局最优解的几何活动轮廓方法出发,分别提出了两种基于齐次Besov窄间与小波变换的图像分割算法,并给出了解的存在性证明.数值求解利用小波软阈值以及分裂Bregman方法,能够有效提高计算效率.由于小波变换具有多分辨特性,对于包含较多细节信息的图像,采用新算法能够得到更好的分割效果.数值实验表明采用新算法能够获得较...     

基于方向多尺度变换和统计建模的纹理分类方法

纹理是图像分析和识别中经常使用的关键特征, 而小波变换则是图像纹理表示和分类中的常用工具. 然而, 基于小波变换的纹理分类方法常常忽略了小波低频子带信息, 并且无法提取图像纹理的块状奇异信息. 本文提出小波子带系数的局部能量直方图建模方法、轮廓波特征的Poisson 混合模型建模方法和基于轮廓波子带系数聚类的特征提取方法, 并将其应用于图像纹理分类上. 基于局部能量直方图的纹理分类方法解决了小波低频子带的建模难题, 基于Poisson 混合模型的纹理分类方法则首次将Poisson 混合模型用于轮廓子带特征的建模, 而基于轮廓波域聚类的纹理分类方法是一种快速的分类方法. 实验结果显示, 本文所提出的三类方法都超过了当前典型的纹理分类方法.     

多尺度函数与多小波的构造

在多小波和单小波的基础上利用矩阵卷积构造出了一类多尺度函数与多小波,并通过实例对构造算法加以说明.     

基于广义交叉认证的多小波阈值的图像降噪

提出一种新的小波收缩阈值降噪方法,该方法是通过对噪声图像进行多小波变换,然后用广义交叉认证的方法来确定小波阈值参数．由于本文采用的是多小波变换,而多小波一般同时具有正交性和线性相位,另外广义交叉认证方法不需要对噪声的强度进行估计,所以这种方法有比较好的降噪效果．实验结果表明该方法与基于小波变换的广义交叉认证的图像降噪方法相比较,其降噪效果有一定的提高;同时也表明在一定的条件下,其降噪效果要明显好于传统的Wiener滤波方法．     

基于多小波预测方法提高地震数据的信噪比

基于二维小波变换把一个二元的地震数据变换为有关时间,空间,频率和波数的局部信息的特点,讨论了用正交多小波变换和w-x预测方法对地震数据进行去噪处理.先用阈值的方法做初步处理,再用预测方法进一步压制噪声,达到较好地提高地震数据信噪比的目的.数值试验表明该方法是有效的,能有效地消除随机干扰,具有很好的空间和时间自适应性.     

基于非下采样Contourlet变换与TV模型混合图像去噪算法

在非下采样Contourlet变换的基础上,综合考虑全变差扩散和正态逆高斯模型,提出一种新的图像去噪算法.首先,对图像进行非下采样Contourlet变换,得到高频子带和低频子带系数.然后,对低频子带进行全变差扩散处理,对于方向带通子带,先通过分类准则对其进行分类,将其分为重要系数和不重要系数,对重要系数采样正态逆高斯建模,不重要系数采用高斯分布模型建模.实验结果证明,本文方法在视觉效果、峰值信噪比以及平均结构性上均优于许多算法.     

Matrix Thresholding for Multiwavelet Image Denoising

Vector thresholding is a recently proposed technique for the denoising of one-dimensional signals by means of multiwavelet shrinkage. It is more suited both to dealing with the multiwavelet vector coefficients and to taking into account the correlations which can be introduced among the starting vector coefficients by the use of a suitable prefilter. Motivated by the successful results of the multiwavelet transform when used in image processing, the aim of this paper is to extend vector thresholding to the two-dimensional case by introducing the notion of matrix thresholding. This new method allows us to easily exploit the matrix nature of the two-dimensional multiwavelet transform, and represents the natural extension of vector thresholding to the 2-D case. Afterwards, as the choice of the threshold level is very important in the practical application of thresholding methods, we propose a first attempt to extend the recently introduced method of H-curve to a multiple wavelet setting. The results of extensive numerical simulations confirm the effectiveness of our proposals and encourage us to keep going in this direction with further studies.     

基于多小波变换提高地震数据的分辨率

给出了设计接近地震子波的小波函数方法,使得地震信号的小波分解能量相对集中,有利于做各种处理;通过选出最匹配子波的的小波函数,做多小波变换;利用分频处理函数来补偿石油勘探中地震数据中损失的高频成分,以提高石油勘探中地震数据分辨率.通过对反射信号的高频成分进行补偿,可使地震剖面的主频得到很大提高,频宽得到扩展,可获得较高分辨率的地震剖面.从实际数据处理结果可以看出,方法是可行的且是有效的.     

Contrast enhancement for image by WNN and GA combining PSNR with information entropy

A new contrast enhancement algorithm for image is proposed combining genetic algorithm (GA) with wavelet neural network (WNN). In-complete Beta transform (IBT) is used to obtain non-linear gray transform curve so as to enhance global contrast for an image. GA determines optimal gray transform parameters. In order to avoid the expensive time for traditional contrast enhancement algorithms, which search optimal gray transform parameters in the whole parameters space, based on gray distribution of an image, a classification criterion is proposed. Contrast type for original image is determined by the new criterion. Parameters space is, respectively, determined according to different contrast types, which greatly shrink parameters space. Thus searching direction of GA is guided by the new parameter space. Considering the drawback of traditional histogram equalization that it reduces the information and enlarges noise and background blur in the processed image, a synthetic objective function is used as fitness function of GA combining peak signal-noise-ratio (PSNR) and information entropy. In order to calculate IBT in the whole image, WNN is used to approximate the IBT. In order to enhance the local contrast for image, discrete stationary wavelet transform (DSWT) is used to enhance detail in an image. Having implemented DSWT to an image, detail is enhanced by a non-linear operator in three high frequency sub-bands. The coefficients in the low frequency sub-bands are set as zero. Final enhanced image is obtained by adding the global enhanced image with the local enhanced image. Experimental results show that the new algorithm is able to well enhance the global and local contrast for image while keeping the noise and background blur from being greatly enlarged.     

2-D NONSEPARABLE SCALING FUNCTIONINTERPOLATION AND APPROXIMATION

1 IntroductionWe begin witl1 two fundanlental questious of apprdriation theory Namely given sam-ples of a square iutegrable signal dyadically spaced in tin1e, is it possible to reconstruct thesignal?How close can the original signal be aPprokimated from the knowledge of the samples?There are many dtherent approaches to answer these questiolls. In [81, Wells and Zhoushowed that a wavelet approalmatiou theorem is valid for degree 1wavelet systenis in whichone obtains second-order approximation…     

Digital watermarking scheme for colour remote sensing image based on quaternion wavelet transform and tensor decomposition

Digital watermarking is important for protecting the intellectual property of remote sensing images. Unlike watermarking in ordinary colour images, in colour remote sensing images, watermarking has an important requirement: robustness. In this paper, a robust nonblind watermarking scheme for colour remote sensing images, which considers both frequency and statistical pattern features, is constructed based on the quaternion wavelet transform (QWT) and tensor decomposition. Using the QWT, not only the abundant phase information can be used to preserve detailed host image features to improve the imperceptibility of the watermark, but also the frequency coefficients of the host image can provide a stable position to embed the watermark. To further strengthen the robustness, the global statistical feature structure acquired through the tensor Tucker decomposition is employed to distribute the watermark's energy among different colour bands. Because both the QWT frequency coefficients and the tensor decomposition global statistical feature structure are highly stable against external distortion, their integration yields the proposed scheme, which is robust to many image manipulations. A simulation experiment shows that our method can balance the trade‐off between imperceptibility and robustness and that it is more robust than the traditional QWT and discrete wavelet transform (DWT) methods under many different types of image manipulations.     

Wavelet Image Compressor—MinImage

Nowadays, still images are used everywhere in the digital world. The shortages of storage capacity and transmission bandwidth make efficient compression solutions essential. A revolutionary mathematics tool, wavelet transform, has already shown its power in image processing. MinImage, the major topic of this paper, is an application that compresses still images by wavelets. MinImage is used to compress grayscale images and true color images. It implements the wavelet transform to code standard BMP image files to LET wavelet image files, which is defined in MinImage. The code is written in C++ on the Microsoft Windows NT platform. This paper illustrates the design and implementation details in Min-Image according to the image compression stages. First, the preprocessor generates the wavelet transform blocks. Second, the basic wavelet decomposition is applied to transform the image data to the wavelet coefficients. The discrete wavelet transforms are the kernel component of MinImage and are discussed in detail. The different wavelet transforms can be plugged in to extend the functionality of MinImage. The third step is the quantization. The standard scalar quantization algorithm and the optimized quantization algorithm, as well as the dequantization, are described. The last part of MinImage is the entropy-coding schema. The reordering of the coefficients based on the Peano Curve and the different entropy coding methods are discussed. This paper also gives the specification of the wavelet compression parameters adjusted by the end user. The interface, parameter specification, and analysis of MinImage are shown in the final appendix.     

一种分形图像编码的改进方法

在分析小波包变换和分形编码特点的基础上,先将图像进行小波包分解,对进一步细分的高频部分直接进行频域截断,对低频部分进行分形压缩.计算机模拟试验表明,上述方案与基本分形编码方法相比,在重建图像主观质量和运行时间上都显示出优越性.