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.

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。

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%。

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.

基于改进DTCWT和引导滤波的低剂量CT图像降噪

针对低剂量计算机断层扫描(CT)图像噪点较多和空间分辨率低的问题,提出了一种基于改进双树复小波变换(DTCWT)和引导滤波的低剂量CT图像降噪方法。首先使用DTCWT对低剂量CT图像进行多尺度分解,对高频子带系数使用拟合优度检验框架进行去噪,对低频子带使用三边滤波进行降噪,逆DTCWT重构得到初始去噪图像;然后使用降噪并插值的低频子带图像作为导向图像对CT图像进行引导滤波;接着结合初始去噪图像和引导滤波的去噪结果生成细节残存图像;最后用3维块匹配滤波(BM3D)阈值算法对细节残存图像进行二次去噪,叠加两次去噪结果得到最终去噪图像。实验结果表明,该方法优于其他传统图像去噪算法,在抑制噪声的同时,良好地保留了图像的边缘轮廓和纹理特征,对低剂量CT图像有着显著的去噪效果。

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.

基于定子电流和电磁转矩双信号融合的齿轮故障智能诊断

在电机驱动的齿轮传动系统中,电机本体具有传感器的特性,因此可以通过电机的定子电流、电磁转矩信号来进行齿轮故障分析,由于受转速和负载转矩的影响,使得故障诊断结果的准确率较低。针对此问题,提出一种基于双信号融合与反向传播神经网络相结合的齿轮故障诊断方法。对电机齿轮传动系统一体化建模,进行电机齿轮传动系统联合仿真。对齿轮的不同故障进行模拟,得到电机侧定子电流和电磁转矩的故障信号,采用双树复小波变换来分析齿轮故障频段信号,提取故障特征量,建立了丰富的齿轮故障样本库。搭建反向传播神经网络并提出改进的自适应学习率算法,实现了对齿轮断齿、磨损故障的精确分类。为了验证所提方法的有效性,搭建齿轮故障试验平台,对相应齿轮故障进行诊断。结果表明,所提方法能够在不同转速和负载转矩条件下准确辨识齿轮的故障类型,相较于只采用定子电流和电磁转矩中一种信号对齿轮进行故障诊断,该方法准确率更高。

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.

双树复小波变换下的数字图像鲁棒性水印方法

为提高数字图像的峰值信噪比,提出基于双树复小波变换的数字图像鲁棒性水印方法。利用双树复小波变换对原始输入图像进行分解,结合小波函数与图像重构条件,降低输出信息的冗余度,并通过计算各子图的高频变量,确定水印嵌入位置,以此为依据,根据嵌入强度因子与水印嵌入密钥实现水印的嵌入,通过图像水印序列的解扩与反置乱操作,对图像水印进行提取,继而完成水印的嵌入与提取过程。实验结果表明,所提方法的峰值信噪比较高,具有良好的鲁棒性。

机器人锅炉冷态空气动力场测量系统开发

针对锅炉冷态空气动力场试验自动化程度低、操作危险性大的问题,开发了机器人锅炉冷态空气动力场试验测量系统。系统下位机采用STM32芯片作为主控芯片,控制爬壁机器人的运动以及与上位机的信息交换,同时引入混沌线性惯性权重对粒子群优化模糊PID算法进行优化,并将改进后的算法作为机器人运动路径的控制策略,对于机械臂的控制引入D-H法。上位机为LabVIEW搭建的操作平台,通过嵌入双树复小波变换去噪算法,对采集到的风速信号进行降噪处理。结果表明:所提出的系统各个模块均可正常且稳定运行,与人工测试的误差保持在±10%,能够满足锅炉冷态试验的要求。

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。

用于低剂量CT图像去噪的多级双树复小波网络

基于卷积神经网络(CNN)的图像去噪方法能有效去除低剂量计算机断层扫描(CT)图像伴随的伪影和噪声,从而确保CT设备输出高质量图像同时降低辐射,这对患者健康和医学诊断具有重要意义。为了进一步提高低剂量CT图像的质量,提出一种小波域去噪网络MDTNet。首先,基于双树复小波变换(DTCWT)构造多级编解码去噪网络,在多个尺度上提取特征以保留更多高频细节;然后,利用扩展的像素重排技术替代卷积上下采样,实现多级输入和特征融合,从而降低计算复杂度;最后,通过大量训练找到最佳的去噪模型,即二级MDTNet配合LeGall滤波器和Qshift_b滤波器,并选择较大尺寸的CT图像作为训练数据。使用AAPM数据集评估MDTNet的性能,实验结果表明,MDTNet能有效去除条纹状伪影和噪声,在定量和定性评估中性能均优于同类型去噪方法。与FWDNet相比,对于1 mm的切片,MDTNet的平均峰值信噪比(PSNR)和结构相似性指数(SSIM)分别提高了0.0887 dB和0.0024;对于3 mm的切片,分别提升了0.1443 dB和0.003。对于单张512×512像素的低剂量CT图像去噪,MDTNet在GPU上仅需0.193 s。MDTNet在保持高效率的同时保留了更多的高频细节,能够为低剂量CT图像去噪提供一种新的框架。

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。