基于复小波变换的地震资料角度域信息提取

局部角度域属性是地震资料非常重要的属性之一,应用地震波在局部角度域的传播特性,提取地震属性,分析介质的物性参数。通过对三维地震数据的深层切片处理获取局部角度域属性,提出采用基于映射的复小波变换对二维地震资料两步算法．提出局部角度域信息的解决方案。     

用对数域电流模式电路实现连续小波变换

本文提出了用对数域电路实现连续小波变换的一种方法.利用复解调技术将设计带通滤波器组转化为设计相对简单的低通滤波器组问题,给出了分解与重构的系统流程.设计并分析了对数域电流控制振荡电路,对数域积分器和高斯滤波器等主要的模拟电路.     

基于混沌映射的小波变换域数字水印算法

本文结合传统的密码学和小波变换域数字水印技术,在水印信息被嵌入到载体作品之前,根据密码学原理将水印进行加密,然后再进行嵌入,充分考虑了小波变换的频域特性与人类视觉系统的很好匹配.在前人的研究基础上,利用图像的小波变换域的JND模型提出了一种基于混沌映射的在图像的小波变换域嵌入数字水印的算法.实验证明,用该算法嵌入的水印...     

基于变换域离散小波变换音频水印算法研究

本文提出基于离散小波域的音频水印算法。对要嵌入水印的音频数据进行小波变换三级分解。水印图像经过置乱处理后,再进行降维处理按乘性规则嵌入到经过三级DWT分解的近似分量中,然后和三级DWT的细节分量一起经过逆变换处理产生嵌入水印的音频信号。通过上述过程的逆变换从嵌入水印的音频信号中提取出水印。     

基于Gabor小波变换复频域响应的人脸识别研究

介绍了Gabor小波变换的基本原理,利用幅值和相位信息,结合描述局部特征的提取方法,使高维数据能被有效处理,最后通过合适的判别标准进行识别比对,在ORL,Feret等人脸库上得到较好的识别率.     

基于小波的变换域通信系统抗干扰性能研究

变换域通信系统通过信道估计在变换域设计信号波形避开干扰,它没有采用载波调制,而是用一个类似噪声的基函数进行信息调制。对高斯白噪声信道下的取不同门限值的基于小波的变换域通信系统与DSSS的抗干扰性能作了比较分析,仿真结果表明基于小波的变换域通信系统相比于DSSS在单音、多音、线性调频、10%部分带宽干扰下误码率平均改进约10dB,在70%部分带宽干扰下误码率平均改进约5dB,并对基于小波的变换域通信系统门限值的选取给出了建议。     

基于小波变换域估计的瞬态信号检测

在未知瞬态信号检测中，一般采用广义似然比检测，然而广义似然比检测不能有效利用瞬态信号的固有特性，检测性能不是很好。文中在介绍瞬态信号和噪声的小波变换特性基础上，提出了基于小波变换域估计的检测方法，最后给出了几种检测器的工作特性比较。     

基于双树复小波变换的自适应图像融合方法

双树复小波具有平移不变性、方向选择性、有限冗余等特点,用于图像融合,优于传统的小波变换方法。本文提出一种基于双树复小波变换的自适应图像融合方法,源图像复小波分解后低频采用PCA,高频采用区域能量算法。通过对可见光和红外图像的融合实验,结果证明了双树复小波的优势和所用融合算法的有效性。     

双树复小波变换及其应用综述

双树复小波变换是为克服通常的离散小波变换的缺陷而提出的。当对应小波基（近似）满足Hilbert变换关系时，双树复小波变换能够极大地减小通常的实小波变换中的平移敏感性，改善方向选择性。这些优点使双树复小波变换成为有效的图像配准融合工具，能够显著提高配准融合质量。     

基于小波变换域上的KL变换的地震信号去噪方法

基于相邻地震道的有效波在波形及能量上有较强的相关性及改变各个频率成分的信噪比．有益于提高分辨率的观点，本文提出了基于小波变换域上的KL变换去噪方法．该方法的主要思想是：将地震记录进行小波分解，形成地震记录的分时、分频小波包剖面，然后利用相邻道相关的性质，用KL变换进行去噪，再将各个小波包剖面重构为去噪后的地震剖面．理论记录计算及实际地震剖面的处理结果表明：基于小波变换的分时、分频KL变换去噪方法能有效地提高地震剖面的信噪比和分辨率．     

Lossless image compression with projection-based and adaptive reversible integer wavelet transforms

Reversible integer wavelet transforms are increasingly popular in lossless image compression, as evidenced by their use in the recently developed JPEG2000 image coding standard. In this paper, a projection-based technique is presented for decreasing the first-order entropy of transform coefficients and improving the lossless compression performance of reversible integer wavelet transforms. The projection technique is developed and used to predict a wavelet transform coefficient as a linear combination of other wavelet transform coefficients. It yields optimal fixed prediction steps for lifting-based wavelet transforms and unifies many wavelet-based lossless image compression results found in the literature. Additionally, the projection technique is used in an adaptive prediction scheme that varies the final prediction step of the lifting-based transform based on a modeling context. Compared to current fixed and adaptive lifting-based transforms, the projection technique produces improved reversible integer wavelet transforms with superior lossless compression performance. It also provides a generalized framework that explains and unifies many previous results in wavelet-based lossless image compression.     

基于小波变换的多尺度SSIM算法

提出一种基于小波变换的多尺度结构相似度的图像质量评价(WWMS-SSIM)算法。首先对原始图像和失真图像分别进行小波变换,在对应的低频子带图像中比较亮度和相关度差异,在对应高频子带图像中比较对比度、相关度和结构差异,然后赋予不同子带图像不同的视觉加权值,最后得到归一化的图像质量评价值。通过对不同失真类型图像以及Live数据库图像的评价,证明了提出的算法(WWMS-SSIM)相对均方误差、峰值信噪比、结构相似度以及改进的结构相似度评价算法更符合人类的主观视觉感受。     

A new framework for complex wavelet transforms

Although the discrete wavelet transform (DWT) is a powerful tool for signal and image processing, it has three serious disadvantages: shift sensitivity, poor directionality, and lack of phase information. To overcome these disadvantages, we introduce two-stage mapping-based complex wavelet transforms that consist of a mapping onto a complex function space followed by a DWT of the complex mapping. Unlike other popular transforms that also mitigate DWT shortcomings, the decoupled implementation of our transforms has two important advantages. First, the controllable redundancy of the mapping stage offers a balance between degree of shift sensitivity and transform redundancy. This allows us to create a directional, non-redundant, complex wavelet transform with potential benefits for image coding systems. To the best of our knowledge, no other complex wavelet transform is simultaneously directional and non-redundant. The second advantage of our approach is the flexibility to use any DWT in the transform implementation. As an example, we can exploit this flexibility to create the complex double-density DWT (CDDWT): a shift-insensitive, directional, complex wavelet transform with a low redundancy of (3/sup m/-1/2/sup m/-1) in m dimensions. To the best of our knowledge, no other transform achieves all these properties at a lower redundancy.     

Multidimensional, mapping-based complex wavelet transforms.

Although the discrete wavelet transform (DWT) is a powerful tool for signal and image processing, it has three serious disadvantages: shift sensitivity, poor directionality, and lack of phase information. To overcome these disadvantages, we introduce multidimensional, mapping-based, complex wavelet transforms that consist of a mapping onto a complex function space followed by a DWT of the complex mapping. Unlike other popular transforms that also mitigate DWT shortcomings, the decoupled implementation of our transforms has two important advantages. First, the controllable redundancy of the mapping stage offers a balance between degree of shift sensitivity and transform redundancy. This allows us to create a directional, nonredundant, complex wavelet transform with potential benefits for image coding systems. To the best of our knowledge, no other complex wavelet transform is simultaneously directional and nonredundant. The second advantage of our approach is the flexibility to use any DWT in the transform implementation. As an example, we exploit this flexibility to create the complex double-density DWT: a shift-insensitive, directional, complex wavelet transform with a low redundancy of (3M - 1)/(2M - 1) in M dimensions. No other transform achieves all these properties at a lower redundancy, to the best of our knowledge. By exploiting the advantages of our multidimensional, mapping-based complex wavelet transforms in seismic signal-processing applications, we have demonstrated state-of-the-art results.     

Fast wavelet transforms based on the extended lapped transform

A new family of wavelet transforms (WTs) based on the extended lapped transform (ELT) is introduced. A new optimisation criterion for the ELT butterfly angles is presented, leading to ELT-based WTs with virtually the same performance but a much lower computational complexity than the standard maximally-regular WTs.<>     

Optimization of integer wavelet transforms based on difference correlation structures.

In this paper, a novel lifting integer wavelet transform based on difference correlation structure (DCCS-LIWT) is proposed. First, we establish a relationship between the performance of a linear predictor and the difference correlations of an image. The obtained results provide a theoretical foundation for the following construction of the optimal lifting filters. Then, the optimal prediction lifting coefficients in the sense of least-square prediction error are derived. DCCS-LIWT puts heavy emphasis on image inherent dependence. A distinct feature of this method is the use of the variance-normalized autocorrelation function of the difference image to construct a linear predictor and adapt the predictor to varying image sources. The proposed scheme also allows respective calculations of the lifting filters for the horizontal and vertical orientations. Experimental evaluation shows that the proposed method produces better results than the other well-known integer transforms for the lossless image compression.     

一种基于小波变换和矢量量化的图像压缩算法

小波变换和矢量量化都是图像压缩中的重要方法。利用小波变换的系数特点,对图像进行小渡分解,对于能量最为集中的低频分量采用标量量化处理,然后将标量量化过程中产生的残差和高频分量一起构造矢量,进行矢量量化。实验结果表明,此算法能够有效提高重构图像质量,获得较高的信噪比。     

Modified distributed arithmetic based architecture for discrete wavelet transforms

A highly area-efficient multiplier-less filterbank architecture for one-and two-dimensional discrete wavelet transforms (DWT) is presented. The look-up table (LUT) structure in traditional distributed arithmetic (DA)-based filter structures has been dissolved to implement a LUT-less DA-DWT with dual filterbanks, which permits the reuse of the same circuitry for both highpass and lowpass filters.     

Parallel pipeline implementation of wavelet transforms

Wavelet transforms have been one of the important signal processing developments in the last decade, especially for applications such as time-frequency analysis, data compression, segmentation and vision. Although several efficient implementations of wavelet transforms have been derived, their computational burden is still considerable. The paper describes two generic parallel implementations of wavelet transforms, based on the pipeline processor farming methodology, which have the potential to achieve real-time performance. Results show that the parallel implementation of the oversampled wavelet transform achieves virtually linear speedup, while the parallel implementation of the discrete wavelet transform (DWT) also outperforms the sequential version, provided that the filter order is large. The DWT parallelisation performance improves with increasing data length and filter order, while the frequency-domain implementation performance is independent of wavelet filter order. Parallel pipeline implementations are currently suitable for processing multidimensional images with data length at least 512 pixels