一种新的多小波图象去噪法

多小波是近年发展起来的一种新的小波理论,它把十分重要的光滑性、紧支性、正交性、对称性等完美地结合起来.本文针对多小波图象去噪问题,对Minimax单小波去噪方法进行了改进,提出了改进Minimax多小波去噪方法,简称IMDM方法.实验数据表明,IMDM去噪方法与直接将Minimax方法用于多小波去噪相比,效果明显优于后者.     

一种新的基于多小波的图像去噪算法

图像去噪是最基本的图像处理技术，小波分析是目前国际上最新的时间．频率分析工具，是信号去噪的强有力处理工具。通过小波变换可以将交织在一起的混合信号分解成不同频率的块信号。多小波所拥有的对称性、正交性、有限支撑等重要特性弥补了单小波的不足。本文提出了一种新的图像去噪方法，该方法以多小波变换为基础。实验证明，具有很好的去噪效果。     

基于航空图像的多小波去噪方法研究

多小波技术把十分重要的光滑性、对称性、正交性、有限支撑等完美地结合起来,弥补了单小波的不足,为信号处理领域开辟了新纪元,尤其在提高军方侦察航空图像的判读效率、减少误判率方面更具有十分重要的意义.本文基于航空图像研究了基于多小波的图像去噪技术,通过分析小波的各种去噪原理和讨论阈值取值的门限问题,利用Matlab快速分解算法,基于CL多小波对加噪声的航空图像进行多小波分解和去噪,并对去噪后的图像进行了比较和分析.实验结果证明多小波去除小噪声图像时,不增加图像跳边缘,不会导致振荡效应,有较好去噪效果.     

一种新的基于多小波变换的图像增强方法

小波分析是目前国际上最新的时间频率分析工具，是信号去噪的强有力处理工具．小波变换可以将交织在一起的混合信号分解成不同频率的块信号．多小波所拥有的对称性、正交性、有限支撑等重要特性弥补了单小波的不足．提出了一种新的图像增强方法，该方法以多小波变换为基础，采用多尺度非线性增强技术进行图像增强．实验证明，增强效果良好．     

基于多通道小波变换的化学信号滤噪方法

基于多小波变换的理论与算法,提出了多小波软阈值去噪算法。用模拟高斯信号对多小波软阈值滤噪方法与单小波软阈值滤噪方法进行了比较,实验结果表明,多小波滤噪方法去噪效果优于单小波。将多小波软阈值滤噪方法用于黄连提取物的5种组分毛细管电泳信号的滤噪,进行滤噪处理后,噪音基本上被消除,峰位置十分清晰,峰的位置、面积及高度基本不变,基线平稳,有利于进一步进行定量计算。     

基于多小波变换的图像去噪

小波分析是目前国际上最新的时间频率分析工具,是信号去噪的强有力处理工具.小波变换可以将交织在一起的混合信号分解成不同频率的块信号.多小波所拥有的对称性、正交性、有限支撑等重要特性弥补了单小波的不足.提出了一种新的图像去噪方法,该方法以多小波变换为基础.实验证明,去噪效果良好.     

多小波变换在声纳图像降噪中的应用研究

与普通光学图像相比,声纳图像受到噪声污染更加严重,为了更好的去除侧扫声纳图像噪声,提高图像质量,保持图像原始信息,该文通过将图像变换到多小波域,结合热传导方程的差分格式与图像的分形维数,提出了一种声纳图像软阈值去噪算法,并将该算法与单小波去噪算法做了比较．该算法只需要含噪图像本身,不需要任何其它先验知识,是一种自适应的去噪算法．仿真试验表明,与单小波去噪算法相比,该算法具有更好的去噪效果,同时较好的保持了声纳图像的原始信息．     

一种改进的多小波相邻系数去噪算法

小波分析理论已经成为信号去噪中的一种重要的工具.多小波因其能同时具有正交性、紧支性、对称性和高的逼近阶等信号处理中十分重要的特性.弥补了单小波的不足,从而具有更为广阔的应用前景.信号经多小波变换后,不但同一尺度内相邻多小波系数间存在着相关性,而且不同尺度间对应位置上的系数也存在相关性.文中对受白噪声污染的信号进行(平移不变)多小波分解,采用相邻系数法确定阈值的同时利用多小波系数尺度间的相关性,来有效地抑制小尺度上部分幅度较大的噪声多小波系数,提出一种改进的多小波相邻系数去噪算法.仿真实验表明,与传统的多小波阈值和相邻系数法去噪相比,该算法可以获得更好的去噪效果.     

基于多小波基的联合图像去噪方法

小波广泛应用于图像去噪,多个小波基联合运用可以把小波的光滑性、紧支性、正交性、对称性等结合起来。首先用多个小波基分别对含噪图像进行分解、阈值处理和重构,得到多幅重构图像,然后对这些图像进行算术平均,得到最终去噪图像。实验结果表明,该方法去噪效果比单一的小波基方法有明显的改善。     

图像去噪的一种新方法

提出了一种图像去噪的新方法。将维纳滤波和小波域滤波结合起来，同其它的去噪方法比较，发现该去噪方法产生了很好的效果。     

Wavelet and multiwavelet watermarking

The main objective of the paper is to provide a like-with-like performance comparison between the wavelet domain and the multiwavelet domain watermarking, under a variety of attacks. The investigation is restricted to balanced multiwavelets. Furthermore, for multiwavelet domain watermarking, both wavelet-style and multiwavelet-style embedding are investigated. It was shown that none of the investigated techniques performs best across the board. The wavelet-style multiwavelet technique is best suited for compression attacks, whereas scalar wavelets are superior under cropping and scaling. The multiwavelet-style multiwavelet is far superior under low-pass filtering. On the basis of these results, it was concluded that for attacks which are likely to affect mid-range frequencies, the wavelets are more suitable than multiwavelets, whereas for attacks which are likely to affect low frequencies or high frequencies, the multiwavelets are the best choice. Furthermore, the multiwavelets generally offer better visual quality than scalar wavelets, for the same peak signal-to-noise ratio (PSNR). This suggests that part of the available channel capacity remains unused, and shows once more the potential of multiwavelets for digital watermarking.     

正交平衡多小波的构造及其在图像编码中的研究

多小波作为小波理论的一个新的分支，具有标量小波所没有的特性．为更好地逼近原始信号，多小波需要选择一个好的预滤波器．在分析GHM和CL多小波的基础上，根据多小波正交条件、对称及反对称性，提出一种正交平衡滤波器构造的方法．图像编码实验结果表明，不需任何预滤波，正交平衡滤波器的性能远远高于GHM和CL，接近或高于9／7标量小波滤波器．     

基于多小波分析的超声斑纹噪声抑制方法研究

多小波是对小波理论的一个新发展,它可以同时满足正交性、对称性、短支撑等良好特性要求,而标量小波不能同时达到这些要求.提出了一种新的基于多小波、多分辨率分析抑制超声斑纹噪声的理论和方法,通过评价两种多小波的分解与重构,得到了两种可以分别适用于这两类多小波的最佳预处理方法,给出了多小波变换的解析表达式.在二维图像阵列的应用方面,通过采用局部方差作为阈值,对超声斑纹图像进行阈值化去噪处理的实验结果表明,多小波变换在有效抑制斑纹噪声的同时,相对于其他方法还能更好地保留原图像的边界等重要特征信息.     

Some applications of Loop-subdivision wavelet tight frames to?the?processing of 3D graphics

Multiresolution analysis based on subdivision wavelets is an important method of 3D graphics processing. Many applications of this method have been studied and developed, including denoising, compression, progressive transmission, multiresolution editing and so on. Recently Charina and St?ckler firstly gave the explicit construction of wavelet tight frame transform for subdivision surfaces with irregular vertices, which made its practical applications to 3D graphics became a subject worthy of investigation. Based on the works of Charina and St?ckler, we present in detail the wavelet tight frame decomposition and reconstruction formulas for Loop-subdivision scheme. We further implement the algorithm and apply it to the denoising, compression and progressive transmission of 3D graphics. By comparing it with the biorthogonal Loop-subdivision wavelets of Bertram, the numerical results illustrate the good performance of the algorithm. Since multiresolution analysis based on subdivision wavelets or subdivision wavelet tight frames requires the input mesh to be semi-regular, we also propose a simple remeshing algorithm for constructing meshes which not only have subdivision connectivity but also approximate the input mesh.     

Supercompact multiwavelets for flow field simulation

A supercompact multiwavelet scheme for computational fluid dynamics is presented. Beam and Warming's supercompact wavelet method is an appropriate wavelet for fluid simulation data in the sense that it can provide compact support. The compactness of the wavelets avoids unnecessary interaction with remotely located data (e.g. across a shock discontinuity or vortex) and significantly reduces computational data processing time. Thresholding for data compression with the supercompact wavelets is applied based on a covariance vector structure of multiwavelets. The extension of this scheme to three dimensions is analyzed. Numerical tests demonstrate that the analytic advantages actually result in large data compression ratios.     

基于斜变换的小波设计及其应用

根据Walsh变换与小波之间的联系,在斜变换的基础上提出了一种新的多进制多小波设计思想,改变了传统小波变换中滤波器设计方法的单一性和推导的复杂性,该滤波器组利用斜变换的快速算法生成简单方便。将该方法应用于图像去噪和图像压缩的实验中,实验结果证明该方法的去噪效果和压缩效果要优于传统的DWT,并且去噪效果还要优于文献[6]中提出的多进制多小波。     

方向性小波理论应用特性分析

方向性小波理论为图像处理提供了一种新的稀疏表示方法，能够更有效地捕捉图像中的几何结构。本文从基的特征入手，比较了方向性小波与传统小波在逼近图像几何边缘时的不同之处；总结了近年来该领域内提出的几种主要理论，以Ridgelet变换为例说明了方向性小波理论的基本原理。实验演示了Contourlet变换和小波变换的非线性逼近性能
能和去噪效果。最后指出了该领域进一步研究的方向     

Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction

Abstract: Electromyography gives an electrical representation of neuromuscular activation associated with a contracting muscle. The electromyography signal acquires noise while travelling though different media. The wavelet transform is employed for removing noise from surface electromyography (SEMG) and higher order statistics are applied for analysing the signal. With the appropriate choice of wavelet, it is possible to remove interference noise (denoise) effectively in order to analyse the SEMG. Daubechies wavelets (db2, db4, db5, db6, db8), symmlet (sym4, sym5) and the orthogonal Meyer (dmey) wavelet can efficiently remove noise from the recorded SEMG signals. However, the most effective wavelet for SEMG denoising is chosen by calculating the root mean square difference and signal-to-noise ratio values. Results for both root mean square difference and signal-to-noise ratio show that wavelet db2 performs denoising best out of the wavelets. Furthermore, the higher order statistics method is applied for SEMG signal analysis because of its unique properties when applied to random time series, such as parameter estimation, testing of Gaussianity and linearity, deterministic and non-deterministic signal detection etc. Gaussianity and linearity tests as part of higher order statistics are conducted to understand changes in muscle contraction and to quantify the effectiveness of the noise removal process. According to the results, the SEMG signal becomes less Gaussian and more linear with increased force.