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 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.

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.

Adaptive Dual-Threshold Edge Detection Based on Wavelet Transform

In order to solve the problems of local maximum modulus extraction and threshold selection in the edge detection of finite resolution digital images, a new wavelet transform based adaptive dual threshold edge detection algorithm is proposed. The local maximum modulus is extracted by linear interpolation in wavelet domain. With the analysis on histogram, the image is filtered with an adaptive dual threshold method, which effectively detects the contours of small structures as well as the boundaries of large objects. A wavelet domain's propagation function is used to further select weak edges. Experimental results have shown the self adaptivity of the threshold to images having the same kind of histogram, and the efficiency even in noise tampered images.

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.

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.

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.

ON NECESSARY AND SUFFICIENT CONDITIONS FOR DUALS OF WAVELET FRAMES

This paper is concerned with the characterization of the duals of wavelet frames of L(2)(R). The sufficient and necessary conditions for them are obtained.

Weighted Variational Minimization Model for Wavelet Domain Inpainting with Primal-Dual Method

To preserve the edges and details of the image,a new variational model for wavelet domain inpainting was proposed which contained a non-convex regularizer. The non-convex regularizer can utilize the local information of image and perform better than those usual convex ones. In addition, to solve the non-convex minimization problem,an iterative reweighted method and a primaldual method were designed. The numerical experiments show that the new model not only gets better visual effects but also obtains higher signal to noise ratio than the recent method.

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。

基于小波双阈值滤波算法的膈肌肌电信号去噪方法

针对传统去心电信号(ECG)干扰算法处理异常ECG信号效果欠佳的问题,提出一种基于小波双阈值滤波算法的膈肌肌电(EMGdi)信号降噪方法。该方法以小波系数为基础,构造QRS群波中R峰的检测阈值,进而准确定位R峰位置。在此基础上,采用干扰区间两侧小波平均能量作为干扰区间阈值计算依据,对干扰区间进行平滑处理。通过临床EMGdi信号去ECG干扰实验,结果表明本文方法在去干扰性能上优于传统算法,尤其针对弱EMGdi信号去干扰优势更加明显。

基于光响应非均匀性的WhatsApp压缩视频来源识别

光响应非均匀噪声(photo response nonuniformity,PRNU)是光学成像传感器成像时引入的一种独特噪声,可有效识别压缩视频的来源。针对现有算法提取压缩视频的PRNU效果并不显著的问题,论文提出了一种改进PRNU提取算法。首先,去除视频编解码的环路滤波器,对视频帧使用双密度双树复小波变换进行分解;然后对高频子带使用基于贝叶斯阈值估计的双变量收缩算法进行估计,再使用自适应加窗维纳滤波进行二次估计,得到噪声残差;最后用基于量化参数值加权的最大似然估计法聚合噪声残差,再与视频帧估计得到PRNU。实验结果表明:该文提出的方法在20 s时WhatsApp视频的识别率为75%。

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。

基于多尺度邻域收缩和结构滤波的加权PRNU模型

光响应非均匀性(Photo-Response Non-Uniformity,PRNU)是一种反映成像传感器缺陷的固有特征,可有效识别拍摄该数字视频的相机来源。针对网络压缩视频识别效果不佳的问题,提出一种基于Stein’s无偏风险估计的多尺度邻域值收缩滤波和自适应边缘结构保持的平滑滤波算法,并构建一个加权PRNU提取模型。该模型首先对跳过环路滤波的视频帧进行基于双树复小波的多尺度变换,使用基于Stein’s无偏风险估计的多尺度邻域值收缩滤波算法估计所有高频子带,得到噪声残差后,利用自适应边缘结构保持平滑滤波对复杂的噪声残差进行平滑处理,采用基于量化参数加权的最大似然估计方法聚合噪声残差得到PRNU的乘性因子,最后经过预处理得到PRNU。在Vision数据集上的实验结果显示,所提出的模型在视频时长为15 s时,移动和旋转参考指纹下的AUC值分别为0.9551和0.9549,Kappa系数分别为0.8403,0.8889和0.9132,均优于现有算法。

改进的自适应小波包风电波动平滑控制策略

针对传统自适应小波包风电功率波动平滑技术存在过度平滑以及储能系统负担加重等问题,提出一种改进的自适应小波包风电功率平滑控制策略。首先,根据不同时间段风电功率波动特性,合理规划滤波时间尺度,并确定满足系统控制要求的最小滤波尺度。其次,利用自适应小波包分解滤波尺度内的风电功率,获得期望并网功率与波动功率。之后,采用由双储能电池与超级电容器组成的混合储能系统对波动功率进行平抑。最后,分析改进的平滑策略在降低储能系统负担方面的优势,并对储能系统的运行寿命进行评估。结果表明,在保证风电功率波动有效平滑的前提下,该策略既能降低储能系统的负担,又能延长其运行寿命。

信号峭度不定干扰下的电缆局部放电模式识别研究

电缆不同类型缺陷引发的放电故障受信号偏斜度、峭度的影响,造成电缆的局部放电信号模式识别效果较差。为此,在信号峭度不定干扰下,提出基于卷积神经网络的电缆局部放电模式识别方法。采用小波阈值去噪方法预处理电缆局部放电信号。运用双树复小波变换提取局部放电信号特征。通过提取偏斜度和峭度刻画信号的分布特征,可以解决信号峭度不定干扰问题。采用卷积神经网络训练提取的特征,并将该特征作为分类器完成电缆局部放电模式识别。试验结果表明,该方法的识别准确率高于95%、识别时间低于5.4 s。该方法应用后具有较好的电缆局部放电模式识别性能。

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。

重型减速器在线监测与故障诊断研究

针对重型减速器故障诊断方法中故障特征不显著、易受外界干扰等问题,提出一种基于多维特征选择和BP神经网络的故障诊断方法。首先设计并布置重型减速器故障模拟实验台,使用多源传感信息采集平台进行信号采集;然后基于采集的振动信号,提出双树复小波变换和多维特征选择的轴承故障诊断方法作为底层诊断逻辑,并搭建BP神经网络模型进行故障识别。结果表明,该方法的诊断准确率为94%,能够满足在实际工况中对重型减速器的故障诊断需求。

基于片段充电数据和DEKF-WNN-WLSTM的锂电池健康状态实时估计

实时准确地评估电动汽车锂电池健康状态(SOH)对电动汽车的稳定行驶至关重要。因此,该文提出一种基于锂电池日常片段充电数据和双扩展卡尔曼滤波-小波神经网络-小波长短时记忆神经网络(DEKF-WNN-WLSTM)的电池全充时间估计模型,进而提高了片段充电数据评估电池健康状态的准确度。首先,设计双扩展卡尔曼滤波预测-校正算法,分别用来估计片段充电数据对应的全充时间和校正扩展卡尔曼滤波的状态初值,以提高估计的准确性。然后,设计了小波神经网络-小波长短时神经网络来学习扩展卡尔曼滤波递推过程的观测值。最后,通过实验仿真,验证了所提算法在锂电池健康状态实时估算中的准确性和有效性。

DTCWPT与TSMAE融合的刀具磨损状态辨识方法

获取高质量的刀具磨损特征信息是识别刀具磨损状态的前提。为克服现有刀具磨损状态辨识方法中特征信息提取不足的问题,提出了一种基于双树复小波包变换(dual-tree complex wavelet packet transform,DTCWPT)、时移多尺度注意熵(time-shifted multiscale attention entropy,TSMAE)和随机森林(random forest,RF)的刀具磨损状态辨识方法。利用实测刀具磨损数据集对所提方法的有效性进行了验证,并从信号分解和特征提取两方面与其他磨损辨识技术进行了对比。结果表明,在特征提取阶段,所提方法展现出极高的效率,分别仅需9.41 s和14.91 s即可完成特征提取。在磨损辨识阶段,多次实验的平均辨识精度分别达到了99.33%和100%,充分证明了该方法不仅能够迅速响应,还能准确地辨识刀具的磨损状态。相较其他方法,所提方法在效率和精度上都有明显的优势,在刀具磨损状态辨识领域具有较高的应用潜力。