基于Canny算子边缘检测的小波阈值去噪方法

本文研究了信号处理中图像去噪的问题.利用小波变换理论提出了一种基于Canny算子边缘检测的小波阈值去噪方法,实验结果表明,该方法在有效去除噪声的同时能够更好地保留图像的边缘.     

基于多特征与卷积神经网络的SAR图像分类方法

针对合成孔径雷达图像的分类优化方法,提出一种基于多特征与卷积神经网络的SAR图像分类方法Canny-WTD-CNN.将Canny算子提取的边缘特征,与小波阈值去噪法提取的小波特征进行自适应融合,作为卷积神经网络的输入;以softmax为分类器,对SAR图像进行分类识别检测.最后利用MSTAR公开数据集的三类目标数据进行试验,并给出该方法与其他方法结果的对比,表明该方法的有效性,识别率达到99.14%.     

小波基的选取对图像去噪的影响

小波图像去噪方法是现代图像处理中的重要组成部分，小波基的不同选取直接影响到去噪的效果．本文在全局阈值的标准下，通过对噪声水平和图像纹理特征的估计，讨论了小波基的正交性和线性相位性对去噪结果的不同影响，提出了选取小波基的近似标准．     

基于PSO_Trainlm BP模型的图像去噪研究

针对在使用BP模型进行图像去噪时,模型存在的对初始权阈值敏感、易陷入局部极小值和收敛速度慢的问题.为了提高模型去噪效率,提出采用改进粒子群神经网络模型进行图像去噪.首先运用改进粒子群算法对BP神经网络权阈值进行初始寻优,再用trainlm BP算法对优化的网络权阈值进一步精确优化,随后建立基于粒子群算法的BP神经网络去噪模型,并将其应用到图像去噪研究中.仿真结果表明,新模型结合了粒子群算法的全局寻优能力和BP算法的局部搜索能力,减小了模型对初始权阈值的敏感性,有效防止了模型陷入局部极小值的可能,提高了图像去噪模型的速度和质量.     

插值细分曲线有理参数点的精确求值

本文提出了求值插值细分曲线上任意有理参数的算法.通过构造与细分格式相关的矩阵,m进制分解给定有理数以及特征分解循环节对应算子乘积,计算得到控制顶点权值,实现对称型静态均匀插值细分曲线的求值.本文给出了四点细分和四点Ternary细分曲线的求值实例.算法可以推广到求值其他非多项式细分格式中.     

基于小波变换的图像去噪方法的研究

小波变换能有效的去除高斯噪声,中值滤波能有效的去除脉冲噪声,两者结合可以更有效的去除高斯噪声和脉冲噪声的混合噪声.当医学图像去除混合噪声时,先进行中值滤波再进行小波去噪的方法优于先进行小波去噪后再进行中值滤波的方法,且去噪后图像视觉效果较好,而且图像均方误差(M SE)也较小.在图像去噪处理中这种方法具有实际应用价值.     

一种改进的四阶偏微分方程图像去噪模型

针对四阶偏微分方程图像去噪模型对图像平滑区域处理造成不平整现象,以及无法去除椒盐噪声的问题.首先对含噪图像进行高斯滤波,然后通过修改扩散系数得到一个改进的四阶偏微分方程图像去噪模型.MATLAB仿真结果表明:新模型与原四阶偏微分方程去噪模型相比,其去噪图像不仅视觉效果好;而且峰值信噪比也高;另外,新模型还能有效去除椒盐噪声.     

一种基于小波分析的各向异性图象去噪方法

本文利用非线性各向异性扩散方程结合小波变换提出一种图象去噪的方法。首先对图像进行离散小波变换,然后对其各个分量分别用各向异性的方法实现去噪。实验结果表明,该方法能够较好的去除噪声的同时,很好的保留边缘信息。     

多小波图像插值算法研究

与单小波变换一样,多小波变换同样具有多分辨分析的特性,1次多小波变换可以将图像分解成4个低频子带和12个高频子带,而且原图像的大小是每个子带的4倍.根据多小波变换的这一特点,利用原图像与经过1次多小波变换后的各高频子带的信息,并考虑各子带的分形维数,提出了一种新颖的灰度图像插值算法.实验结果表明,与传统的插值算法相比,例如双线性插值与双三次多项式插值,该算法的插值效果较好,且克服了单小波插值中出现的斑点干扰.     

基于小波域中值滤波和运动估计的去隔行算法

本文提出了一种新的自适应去隔行算法,该方法首先将小波分解引入到去隔行算法的预处理阶段,然后利用运动估计以及混合中值滤波的特点,充分考虑相邻像素间的方向空间相关性,有效保持图像中的边界部分,并减小了运动补偿插值后的误差,尤其是对纹理信息丰富的地方得到了很好的插值效果.实验结果表明,无论是从客观上的信噪比还是主观测评来分析,图像的效果比传统的算法有了提高,能更好的满足人类对画面质量的要求.     

Variational image inpainting

Inpainting is an image interpolation problem with broad applications in image and vision analysis. Described in the current expository paper are our recent efforts in developing universal inpainting models based on the Bayesian and variational principles. Discussed in detail are several variational inpainting models built upon geometric image models, the associated Euler‐Lagrange PDEs and their geometric and dynamic interpretations, as well as effective computational approaches. Novel efforts are then made to further extend this systematic variational framework to the inpainting of oscillatory textures, interpolation of missing wavelet coefficients as in the wireless transmission of JPEG2000 images, as well as light‐adapted inpainting schemes motivated by Weber's law in visual perception. All these efforts lead to the conclusion that unlike many familiar image processors such as denoising, segmentation, and compression, the performance of a variational/Bayesian inpainting scheme much more crucially depends on whether the image prior model well resolves the spatial coupling (or geometric correlation) of image features. As a highlight, we show that the Besov image models appear to be less interesting for image inpainting in the wavelet domain, highly contrary to their significant roles in thresholding‐based denoising and compression. Thus geometry is the single most important keyword throughout this paper. © 2005 Wiley Periodicals, Inc.     

一种新的细分小波及其应用

在图形图像数据传输与数据处理过程中,数据鼍过大是造成不便的主要原因,因此用少量的数据更好地表现图形图像特征是人们追求的目标．图形简化的任务是在保留图形特征的同时删除过多的采样点．简化的中心问题是简化模板的选择,王国谨等人介绍了基于球面多边形逼近的曲面简化技术等方法．用小波技术进行图形简化也是目前图形图像处理过程中的常用方法,如孙延奎等人研究了B样条曲线的多分辨率表示,LounsberyM．等人研究了任意拓扑结构的曲面多分辨分析问题等等．     

曲线的一类非线性多分辨率算法及其收敛性

在曲线的多分辨率分析基础上,构造了一种新的非线性三分多分辨率算法.并研究这个正则三分多分辨率算法的收敛性和稳定性,进一步,证明了小波参数的收敛性精密地依靠这个基本的多分辨率细分算法的收敛性.     

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．     

Construction of parameterizations of masks for tight wavelet frames with two symmetric/antisymmetric generators and applications in image compression and denoising

In this paper, we present a general construction framework of parameterizations of masks for tight wavelet frames with two symmetric/antisymmetric generators which are of arbitrary lengths and centers. Based on this idea, we establish the explicit formulas of masks of tight wavelet frames. Additionally, we explore the transform applicability of tight wavelet frames in image compression and denoising. We bring forward an optimal model of masks of tight wavelet frames aiming at image compression with more efficiency, which can be obtained through SQP (Sequential Quadratic Programming) and a GA (Genetic Algorithm). Meanwhile, we present a new model called Cross-Local Contextual Hidden Markov Model (CLCHMM), which can effectively characterize the intrascale and cross-orientation correlations of the coefficients in the wavelet frame domain, and do research into the corresponding algorithm. Using the presented CLCHMM, we propose a new image denoising algorithm which has better performance as proved by the experiments.     

Statistically based multiwavelet denoising

In this work, we consider a statistically based multiwavelet thresholding method which acts on the empirical wavelet coefficients in groups, rather than individually, in order to obtain an edge-preserving image denoising technique. Our strategy allows us to exploit the dependencies between neighboring coefficients to make a simultaneous thresholding decision, so that estimation accuracy is increased.

By interpreting the multiwavelet analysis in a statistical context, we propose a new weighted multiwavelet matrix thresholding rule, based on the statistical modeling of empirical coefficients. This allows the thresholding decision to be adapted to the local structure of the underlying image, hence producing edge-preserving denoising. Extensive numerical results are presented showing the performance of our denoising procedure.     

Mean size formula of wavelet subdivision tree on Heisenberg group

The purpose of this paper is to investigate the mean size formula of wavelet packets （wavelet subdivision tree） on Heisenberg group. The formula is given in terms of the p-norm joint spectral radius. The vector refinement equations on Heisenberg group and the subdivision tree on the Heisenberg group are discussed. The mean size formula of wavelet packets can be used to describe the asymptotic behavior of norm of the subdivision tree.     

过抽样M通道小波星图去噪方法研究

小波图像去噪已经成为目前图像去噪的主要方法之一,在分析了小波变换的基本理论和小波变换的多尺度分析基础上,根据多尺度小波变换的多分辨特性,提出了过抽样M通道小波变换去噪方法,并将此方法用于星图降噪处理中,收到良好的效果.     

A piecewise polynomial approach to analyzing interpolatory subdivision

The four-point interpolatory subdivision scheme of Dubuc and its generalizations to irregularly spaced data studied by Warren and by Daubechies, Guskov, and Sweldens are based on fitting cubic polynomials locally. In this paper, we analyze the convergence of the scheme by viewing the limit function as the limit of piecewise cubic functions arising from the scheme. This allows us to recover the regularity results of Daubechies et al. in a simpler way and to obtain the approximation order of the scheme and its first derivative.