Quantization-Based Robust Image Watermarking Using the Dual Tree Complex Wavelet Transform

Conventional quantization index modulation (QIM) watermarking uses the fixed quantization step size for the host signal.This scheme is not robust against geometric distortions and may lead to poor fidelity in some areas of content.Thus,we proposed a quantization-based image watermarking in the dual tree complex wavelet domain.We took advantages of the dual tree complex wavelets (perfect reconstruction,approximate shift invariance,and directional selectivity).For the case of watermark detecting,the probability of false alarm and probability of false negative were exploited and verified by simulation.Experimental results demonstrate that the proposed method is robust against JPEG compression,additive white Gaussian noise (AWGN),and some kinds of geometric attacks such as scaling,rotation,etc.

A Dual Tree Complex Discrete Cosine Harmonic Wavelet Transform (ADCHWT) and Its Application to Signal/Image Denoising

A new simple and efficient dual tree analytic wavelet transform based on Discrete Cosine Harmonic Wavelet Transform DCHWT (ADCHWT) has been proposed and is applied for signal and image denoising. The analytic DCHWT has been realized by applying DCHWT to the original signal and its Hilbert transform. The shift invariance and the envelope extraction properties of the ADCHWT have been found to be very effective in denoising speech and image signals, compared to that of DCHWT.

Image inpainting using complex 2-D dual-tree wavelet transform

The dual-tree complex wavelet transform is a useful tool in signal and image process- ing. In this paper, we propose a dual-tree complex wavelet transform （CWT） based algorithm for image inpalnting problem. Our approach is based on Cai, Chan, Shen and Shen＇s framelet-based algorithm. The complex wavelet transform outperforms the standard real wavelet transform in the sense of shift-invariance, directionality and anti-aliasing. Numerical results illustrate the good performance of our algorithm.

Seismic signal analysis based on the dual-tree complex wavelet packet transform

We tried to apply the dual-tree complex wavelet packet transform in seismic signal analysis. The complex wavelet packet transform (CWPT) combine the merits of real wavelet packet transform with that of complex continuous wavelet transform (CCWT). It can not only pick up the phase information of signal, but also produce better ″focal- izing″ function if it matches the phase spectrum of signals analyzed. We here described the dual-tree CWPT algo- rithm, and gave the examples of simulation and actual seismic signals analysis. As shown by our results, the dual-tree CWPT is a very effective method in analyzing seismic signals with non-linear phase.

Defects Recognition of 3D Braided Composite Based on Dual-Tree Complex Wavelet Packet Transform

Textile-reinforced composites,due to their excellent highstrength-to-low-mass ratio, provide promising alternatives to conventional structural materials in many high-tech sectors. 3D braided composites are a kind of advanced composites reinforced with 3D braided fabrics; the complex nature of 3D braided composites makes the evaluation of the quality of the product very difficult. In this investigation,a defect recognition platform for 3D braided composites evaluation was constructed based on dual-tree complex wavelet packet transform( DT-CWPT) and backpropagation( BP) neural networks. The defects in 3D braided composite materials were probed and detected by an ultrasonic sensing system. DT-CWPT method was used to analyze the ultrasonic scanning pulse signals,and the feature vectors of these signals were extracted into the BP neural networks as samples. The type of defects was identified and recognized with the characteristic ultrasonic wave spectra. The position of defects for the test samples can be determined at the same time. This method would have great potential to evaluate the quality of 3D braided composites.

A New Construction Method for the Dual Tree Complex Wavelet Based on Direction Sensitivity

The conception of 'main direction' of multi-dimensional wavelet is established in this paper, and the capabilities of several classical complex wavelets for representing directional singularities are investigated based on their main directions. It is proved to be impossible to represent directional singularities optimally by a multi-resolution analysis (MRA) of L2(R2). Based on the above results, a new algorithm to construct Q-shift dual tree complex wavelet is proposed. By optimizing the main direction of parameterized wavelet filters, the difficulty in choosing stop-band frequency is overcome and the performances of the designed wavelet are improved too. Furthermore, results of image enhancement by various multi-scale methods are given, which show that the new designed Q-shift complex wavelet do offer significant improvement over the conventionally used wavelets. Direction sensitivity is an important index to the performance of 2D wavelets.

NEW METHOD OF EXTRACTING WEAK FAILURE INFORMATION IN GEARBOX BY COMPLEX WAVELET DENOISING

Because the extract of the weak failure information is always the difficulty and focus of fault detection. Aiming for specific statistical properties of complex wavelet coefficients of gearbox vibration signals, a new signal-denoising method which uses local adaptive algorithm based on dual-tree complex wavelet transform （DT-CWT） is introduced to extract weak failure information in gear, especially to extract impulse components. By taking into account the non-Gaussian probability distribution and the statistical dependencies among wavelet coefficients of some signals, and by taking the advantage of near shift-invariance of DT-CWT, the higher signal-to-noise ratio （SNR） than common wavelet denoising methods can be obtained. Experiments of extracting periodic impulses in gearbox vibration signals indicate that the method can extract incipient fault feature and hidden information from heavy noise, and it has an excellent effect on identifying weak feature signals in gearbox vibration signals.

Tracking of Non-Rigid Object in Complex Wavelet Domain

In this paper we have proposed an object tracking method using Dual Tree Complex Wavelet Transform (DTCxWT). The proposed method is capable of tracking the moving object in video sequences. The object is assumed to be deform-able under limit i.e. it may change its shape from one frame to another. The basic idea in the proposed method is to decompose the image into two components: a two dimensional motion and a two dimensional shape change. The motion component is factored out while the shape is explicitly represented by storing a sequence of two dimensional models. Each model corresponds to each image frame. The proposed method performs well when the change in the shape in the consecutive frames is small however the 2-D motion in consecutive frames may be large. The proposed algorithm is capable of handling the partial as well as full occlusion of the object.

Recognition of Group Activities Using Complex Wavelet Domain Based Cayley-Klein Metric Learning

A group activity recognition algorithm is proposed to improve the recognition accuracy in video surveillance by using complex wavelet domain based Cayley-Klein metric learning.Non-sampled dual-tree complex wavelet packet transform(NS-DTCWPT)is used to decompose the human images in videos into multi-scale and multi-resolution.An improved local binary pattern(ILBP)and an inner-distance shape context(IDSC)combined with bag-of-words model is adopted to extract the decomposed high and low frequency coefficient features.The extracted coefficient features of the training samples are used to optimize Cayley-Klein metric matrix by solving a nonlinear optimization problem.The group activities in videos are recognized by using the method of feature extraction and Cayley-Klein metric learning.Experimental results on behave video set,group activity video set,and self-built video set show that the proposed algorithm has higher recognition accuracy than the existing algorithms.

基于DTCWT-VAE的弹道中段目标RCS识别

针对弹道目标雷达信号易受环境影响、目标识别准确率低的问题,提出了一种基于双树复小波变换(dual-tree complex wavelet transform,DTCWT)和变分自编码器(variational autoencoder,VAE)的弹道目标雷达散射截面(radar cross section,RCS)识别法。首先,采用DTCWT对弹道目标RCS动态数据进行预处理,再利用VAE提取目标的隐变量特征,最后用支持向量机(support vector machine,SVM)分类器进行识别。实验结果表明,与已有方法相比,该方法具有更高的识别概率,且鲁棒性较好。

Insect recognition based on integrated region matching and dual tree complex wavelet transform

To provide pest technicians with a convenient way to recognize insects,a novel method is proposed to classify insect images by integrated region matching (IRM) and dual tree complex wavelet transform (DTCWT).The wing image of the lepidopteran insect is preprocessed to obtain the region of interest (ROI) whose position is then calibrated.The ROI is first segmented with the k-means algorithm into regions according to the color features,properties of all the segmented regions being used as a coarse level feature.The color image is then converted to a grayscale image,where DTCWT features are extracted as a fine level feature.The IRM scheme is undertaken to find K nearest neighbors (KNNs),out of which the nearest neighbor is searched by computing the Canberra distance of DTCWT features.The method was tested with a database including 100 lepidopteran insect species from 18 families and the recognition accuracy was 84.47%.For the forewing subset,a recognition accuracy of 92.38% was achieved.The results showed that the proposed method can effectively solve the problem of automatic species identification of lepidopteran specimens.

基于颜色直方图及双树复小波变换(DTCWT)的昆虫图像识别

为了给生产单位害虫管理的普通技术人员提供简便易操作的昆虫鉴别方法,本文提出了一种新颖的基于图像颜色及纹理特征的昆虫图像识别方法。鳞翅目昆虫翅面图像经过预处理,确定目标区域,再进行特征提取。首先将彩色图像从三原色(red-green-blue,RGB)空间转换至色调饱和值(HSV)空间并提取有效区域内的色度、饱和度直方图特征,然后经图像位置校准,提取灰度图的双树复小波变换(DTCWT)特征;匹配首先计算两颜色直方图特征向量之间的相关性,将相关性大于阈值的样本再进一步用DTCWT特征匹配;DTCWT匹配通过计算Canberra距离实现,从通过第一层颜色匹配的样本中取出最近邻作为最终匹配类别。算法在包含100类鳞翅目昆虫的图像库中进行试验验证,取得了76%的识别率,其中前翅识别率则达92%,同时取得了理想的时间性能。试验结果证明了本文方法的有效性。

基于循环平移和DTCWT的声呐图像滤波方法

声呐图像散斑噪声的存在严重影响声呐图像的人工判读和自动识别。声呐图像散斑噪声滤波是声呐图像处理领域的一个重要难题。双树复小波变换(DTCWT)因其具有近似的平移不变性、良好的方向选择性已被应用于图像滤波。但这种平移不变性的近似性使得DTCWT不能较为彻底地抑制伪吉布斯现象,而导致滤波后图像的边缘存在一定程度的模糊。为了减小伪吉布斯现象和声呐图像边缘模糊,将循环平移与DTCWT结合使用,提出一种基于循环平移和DTCWT的声呐图像滤波方法。实验结果表明,该方法在滤除声呐图像散斑噪声的同时能够较好地保持声呐图像的边缘。

基于DTCWT和稀疏表示的红外偏振与光强图像融合

针对红外偏振与光强图像彼此包含共同信息和特有信息的特点,提出了一种基于双树复小波变换和稀疏表示的图像融合方法.首先,利用双树复小波变换获取源图像的高频和低频成分,并用绝对值最大值法获得融合的高频成分;然后,用低频成分组成联合矩阵,并使用K-奇异值分解法训练该矩阵的冗余字典,根据该字典求出各个低频成分的稀疏系数,通过稀疏系数中非零值的位置信息判断共有信息和特有信息,并分别使用相应的规则进行融合;最后,将融合的高低频系数经过双树复小波反变换得到融合图像.实验结果表明,本文提出的融合算法不仅能较好地凸显源图像的共有信息,而且能很好地保留它们的特有信息,同时,融合图像具有较高的对比度和细节信息.

基于改进DTCWT的GIS局部放电信号降噪方法的研究

GIS局部放电产生微弱的特高频信号在外界各种噪声干扰下容易被掩盖,为此利用特高频法检测GIS局部放电必须经过有效降噪处理。文中提出一种基于改进的对偶树复小波(dual-tree complex wavelettransform,DTCWT)局部放电特高频信号降噪方法,该方法是利用DTCWT将含噪信号分解为一系列小波系数,并得到信号在不同变换尺度下的细节分量。利用时域峭度和包络谱峭度将细节分量进行敏感预筛选,极大地提高了后续的最大峭度解卷积(maximum kurtosis deconvolution,MKD)去噪效率。为了验证该方法的有效性与可靠性,进行GIS局部放电特高频信号降噪实测试验,结果表明该方法能有效的对局放信号进行去噪处理,且在较好发挥DTCWT平移不变性等优点的基础上,对细节信号分量的筛选,可改善该局放降噪方法的降噪效果评价指标,并保持其原有的信号特征。

基于DTCWT和LBP的低分辨率人脸识别

针对短时傅里叶变换频率分辨率较差的缺点,提出一种基于双树复小波变换(DTCWT)和局部二进制模式(LBP)直方图的低分辨率人脸识别方法。使用DTCWT获得人脸图像的多尺度多方向的频率幅度响应,采用LBP获取频率幅度响应的统计直方图,通过基于统计的一致性模式得到更加紧凑的统计分布特征。实验结果表明,该方法在低分辨率人脸上可以达到较高的识别准确率。

基于DTCWT和CSP的脑电信号特征提取

针对运动想象脑电信号中存在很多与运动想象无关的频率信号和共空间模式特征提取方法缺少频率信息处理的问题,提出了一种双树复小波变换与共空间模式相结合的特征提取方法.该方法首先选取C3、Cz、C4 3个通道的脑电信号进行上采样,并利用双树复小波变换多尺度分解,获取适当的频段并在相应尺度下进行信号重构;然后将各频段的三通道重构信号联合输入到空间滤波器以得到所需的六维特征向量;最后利用支持向量机来完成两类运动想象任务的分类.采用BCI CompetitionⅣ提供的Dataset 1数据进行实验验证,与CSP、FBCSP、WPD-CSP方法进行比较,7名受试者的训练数据平均分类正确率可达到96.0%,测试数据平均分类正确率达到86.7%.实验结果表明了所提方法的有效性.

基于DTCWT的运动想象脑电特征提取

针对脑电信号采用单一特征识别存在自适应性差和识别率低等问题,提出一种基于双树复小波(DTCWT)的多特征融合的左右手运动想象脑电特征提取方法。对原始脑电信号进行DTCWT变换提取最佳时频段;对所提取的信号频段进行希尔伯特变换与Lempel-Ziv复杂度计算,将得到的时-频域特征与非线性特征组合为特征向量;采用线性判别分析(LDA)完成运动想象任务的分类。实验采用BCI CompetitionⅢ竞赛数据对该方法进行验证,仿真结果表明其识别准确率明显提高,最高可达89.84%。

基于DTCWT域统计特征融合的纹理图像检索

在多尺度变换域,将各子带系数的统计特征进行互补融合可以有效地提高纹理图像检索的性能。文章利用双树复小波变换提出一种新的将低频子带系数的能量特征、高频子带幅值系数的Weibull分布参数特征以及相对相位系数的wrapped Cauchy分布参数特征相融合的纹理图像检索方法,采用VisTex纹理图像库进行检索。结果表明:采用多类系数统计特征的互补融合,以及最优的相似性测度加权组合,能够显著地提高纹理图像检索系统的平均检索率;与现有的7种纹理图像检索方法相比较,所获得的较高平均检索率为86.74%。