基于张量分解与非下采样Contourlet变换遥感图像增强

图像质量低、特征信息不明显是遥感图像获取过程中的常见问题。传统的图像增强方法常常因为不能有效地整合全局信息,从而不能高精度、高效率地凸显有用信息。本文通过结合张量分解和非下采样Contourlet变换,提出一种改进的遥感图像增强方法。使用优化的非下采样Contourlet变换对原始图像进行分解,将各尺度和方向的高频细节图像组合成高阶张量。通过贝叶斯概率张量补全,从不完全张量中识别潜在因子,以预测图像缺失的细节信息。实验结果表明:所提出方法能在有效恢复样张缺失信息的同时突出图像的特征信息,与不同图像增强方法相比,样张处理后在信噪比、结构相似度以及均方根误差方面最大提升分别为27.9%、37.6%和45.4%。改进的遥感图像增强方法在可视化比较和定量评价方面优于常用的图像增强方法。

EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms

The visual analysis of common neurological disorders such as epileptic seizures in electroencephalography(EEG) is an oversensitive operation and prone to errors,which has motivated the researchers to develop effective automated seizure detection methods.This paper proposes a robust automatic seizure detection method that can establish a veritable diagnosis of these diseases.The proposed method consists of three steps:(i) remove artifact from EEG data using Savitzky-Golay filter and multi-scale principal component analysis(MSPCA),(ii) extract features from EEG signals using signal decomposition representations based on empirical mode decomposition(EMD),discrete wavelet transform(DWT),and dual-tree complex wavelet transform(DTCWT) allowing to overcome the non-linearity and non-stationary of EEG signals,and(iii) allocate the feature vector to the relevant class(i.e.,seizure class "ictal" or free seizure class "interictal") using machine learning techniques such as support vector machine(SVM),k-nearest neighbor(k-NN),and linear discriminant analysis(LDA).The experimental results were based on two EEG datasets generated from the CHB-MIT database with and without overlapping process.The results obtained have shown the effectiveness of the proposed method that allows achieving a higher classification accuracy rate up to 100% and also outperforms similar state-of-the-art methods.

Monitoring of Wind Turbine Blades Based on Dual-Tree Complex Wavelet Transform

Structural health monitoring(SHM)in-service is very important for wind turbine system.Because the central wavelength of a fiber Bragg grating(FBG)sensor changes linearly with strain or temperature,FBG-based sensors are easily applied to structural tests.Therefore,the monitoring of wind turbine blades by FBG sensors is proposed.The method is experimentally proved to be feasible.Five FBG sensors were set along the blade length in order to measure distributed strain.However,environmental or measurement noise may cover the structural signals.Dual-tree complex wavelet transform(DT-CWT)is suggested to wipe off the noise.The experimental studies indicate that the tested strain fluctuate distinctly as one of the blades is broken.The rotation period is about 1 s at the given working condition.However,the period is about 0.3 s if all the wind blades are in good conditions.Therefore,strain monitoring by FBG sensors could predict damage of a wind turbine blade system.Moreover,the studies indicate that monitoring of one blade is adequate to diagnose the status of a wind generator.

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.

Multi-scale separation of aeromagnetic abnormality based on dual-tree complex wavelet

Bit-field separation is an important part of gravity and magnetic data processing.In order to extract different levels of anomaly information better,this paper introduces the dual-tree complex wavelet multi-scale separation to the processing of bit-field data firstly and uses the geological model of different buried depth to ve-rify its feasibility.Finally,the dual-tree complex wavelet is applied to the aeromagnetic anomaly in Jinchuan copper nickel mining area.The results show that the method can effectively separate the anomaly information of different scales and analyze the output results with relevant geological data.

Low-amplitude structure recognition method based on non-subsampled contourlet transform

Currently, horizontal well fracturing is indispensable for shale gas development. Due to the variable reservoir formation morphology, the drilling trajectory often deviates from the high-quality reservoir,which increases the risk of fracturing. Accurately recognizing low-amplitude structures plays a crucial role in guiding horizontal wells. However, existing methods have low recognition accuracy, and are difficult to meet actual production demand. In order to improve the drilling encounter rate of high-quality reservoirs, we propose a method for fine recognition of low-amplitude structures based on the non-subsampled contourlet transform(NSCT). Firstly, the seismic structural data are analyzed at multiple scales and directions using the NSCT and decomposed into low-frequency and high-frequency structural components. Then, the signal of each component is reconstructed to eliminate the low-frequency background of the structure, highlight the structure and texture information, and recognize the low-amplitude structure from it. Finally, we combined the drilled horizontal wells to verify the low-amplitude structural recognition results. Taking a study area in the west Sichuan Basin block as an example, we demonstrate the fine identification of low-amplitude structures based on NSCT. By combining the variation characteristics of logging curves, such as organic carbon content(TOC), natural gamma value(GR), etc., the real structure type is verified and determined, and the false structures in the recognition results are checked. The proposed method can provide reliable information on low-amplitude structures for optimizing the trajectory of horizontal wells. Compared with identification methods based on traditional wavelet transform and curvelet transform, NSCT enhances the local features of low-amplitude structures and achieves finer mapping of low-amplitude structures, showing promise for application.

一种基于Contourlet递归Cycle Spinning的图像去噪方法

综合利用Contourlet变换和递归Cycle Spinning,提出一种新的图像去噪方法.由于Contourlet变换缺乏平移不变性,直接进行Contourlet系数阈值图像去噪会产生伪吉布斯现象(导致图像失真),本文引入递归Cycle Spinning来有效地消除这种由于Contourlet变换缺乏平移不变性而产生的图像失真.实验结果显示,与小波递归Cycle Spinning图像去噪等方法相比,该方法明显改善图像视觉效果,显著提高图像的PSNR值.

基于非抽样Contourlet变换的自适应阈值图像增强算法

提出了一种基于非抽样Contourlet变换的自适应阈值图像增强算法,首先对图像进行非抽样Contourlet变换得到不同尺度不同方向上的变换系数,然后由变换系数自适应地确定阈值和调整增强函数,并对变换系数做增强处理,最后对增强处理后的变换系数进行反变换,实现图像增强.实验结果表明,与其他基于变换域的算法相比,该算法可以得到更好的增强效果.

基于非抽样Contourlet变换的图像模糊取证

数字图像被动盲取证技术是对图像的完整性和真实性进行鉴别.图像遭受篡改操作后,为了消除图像伪造在拼接边缘产生畸变,伪造者通常会采用后处理消除伪造痕迹,其中,模糊操作是最常用的手法之一.因此提出了一种针对人工模糊的取证方法.首先,利用非抽样Contourlet变换分析图像边缘点特征进行边缘点分类;然后通过统计正常边缘点与模糊边缘点之间的差异鉴别模糊边缘;最后引入局部清晰度来区分人工模糊与离焦模糊,从而最终标定人工篡改边缘痕迹.实验表明该方法能够有效地检测出图像人工模糊篡改操作,较为准确地定位图像篡改边界.伪造图像边缘模糊越严重方法的检测效果越好.与其他模糊检测方法相比所提方法具有像素级别定位能力.

基于Contourlet区域对比度的遥感图像融合算法(英文)

为综合利用多传感器遥感图像之间的信息,提出了一种基于contourlet变换的遥感图像融合算法。Contourlet是近年发展起来的一种多尺度几何分析工具,比小波变换更适合对图像等二维信号进行处理。算法的思想是将源图像分别进行contourlet变换,在contourlet域完成图像信息的融合,最后通过逆变换得到融合结果。根据分解系数特性,首次提出了contourlet域的区域对比度作为高频子带的融合规则,系数选择更符合人类视觉系统的要求,使得融合图像更有意义。低频子带采用基于区域能量的加权平均作为系数融合规则,在充分保留图像主要信息的前提下进一步增强了算法的稳定性。实验结果表明该算法在性能上明显优于PCA及小波图像融合等传统算法,且能够有效增强源图像的对比度和细节信息。

基于Contourlet变换自适应阈值的图像去噪算法

综合利用Contourlet变换和图像在各个尺度各个方向上的轮廓细节的大小,一定程度上改进了Donoho阈值"过扼杀"其分解系数的缺点,同时还考虑图像的轮廓细节.实验结果表明,与小波阈值,Contourlet阈值和多尺度Contourlet阈值相比,这两种方法更好的提取了图像的轮廓细节,提高了图像的PSNR值.

基于Contourlet变换的遥感影像融合算法

针对目前最新发展的Contourlet变换能比小波变换更适合于进行多尺度边缘增强处理的特点,本文提出了一种新的基于Contourlet变换的用于融合遥感全色和多光谱影像的算法,分别对应于Contourlet变换后得到的低频和高频分量系数,结合小波变换采用了不同的融合规则.实验结果表明本文提出的融合算法能在保留多光谱影像光谱信息的同时增强了融合图像的空间细节表现能力和信息量,该算法是有效可行的.

基于Contourlet变换尺度间相关的图像去噪

Contourlet域数据分析表明,信号的变换域系数在尺度间相关性高,而白噪声则呈弱相关或不相关。通过相关性强弱区分噪声与信号系数,并结合阈值函数,提出了基于Contourlet变换尺度间相关的图像去噪新算法。实验结果表明,新方法去噪后的图像比小波相关去噪算法的PSNR值更高,视觉效果更好,尤其适用于纹理轮廓丰富的图像去噪。

基于区域特性的Contourlet域多聚焦图像融合算法

利用Contourlet变换的多尺度、局部化、方向性和各向异性等优点,提出了一种基于区域特性的Contourlet域多聚焦图像融合算法.该算法将源图像分解至Contourlet变换域,在不同尺度、不同方向的子带中结合区域特性进行图像融合,低频和高频子带中分别采用区域方差和区域能量作为融合规则,最后通过反变换得到融合图像.实验结果表明,所提出的算法能够更好地提取原始图像特征,融合后的图像具有更好的主观视觉效果,与经典的梯度金字塔算法和小波变换算法相比,新算法的均方误差最大值仅为前二者的34.8%和42.6%.

采用非采样Contourlet变换与区域分类的红外和可见光图像融合

提出基于多尺度变换和区域相结合的红外与可见光图像融合方法,用于有效保留红外图像与可见光图像中的空间信息及热目标信息,提升融合图像的可观测性和可理解性。首先,基于非采样Contourlet变换(NSCT)方法对红外和可见光图像进行初步融合,采用基于局部能量的规则融合低通子带系数,根据尺度内各方向子带的相关性原则融合带通方向子带系数。然后,计算初次融合后所得的融合图像与源图像的结构相似性(SSIM),根据源图像与初次融合图像的结构相似程度对图像进行区域分类,得到相似区域分类标识图。最后,依据区域内各自的相似度特性,分别采用不同的融合策略进行二次融合,从而得到最终的融合结果。实验结果表明:该方法能够充分提取源图像的区域特征和纹理特征,融合结果在主观和客观评价上均优于目前流行的融合方法。与仅使用NSCT法进行融合相比,实验所采用的两组图像的质量评价指标分别提高了16%、85%、54%、36%和18%、102%、84%、41%。表明该方法在主客观评价上均优于双树复杂小波变换(DTCWT)、NSCT、冗余离散小波变换(RDWT)等方法。

非下采样Contourlet域自适应阈值面的磁瓦表面缺陷检测

为提高磁瓦表面缺陷人工检测效率、防止缺陷漏检,针对经典缺陷检测算法不能很好地提取颜色暗、对比度低的磁瓦图像缺陷问题,提出一种非下采样Contourlet域自适应阈值面的磁瓦缺陷自动检测方法.该方法根据非下采样Contourlet变换(NSCT)子带系数所在不同区域的特性,采用线扫描的方式对NSCT系数进行阈值处理,给出不同尺度、不同方向的归一化自适应阈值面;并与原始NSCT各子带归一化系数对比分割,以实现对磁瓦图像NSCT系数逐列自适应修正;最后重构NSCT系数提取出磁瓦缺陷.实验结果表明,文中方法能够有效地去除磁瓦表面纹理,提取出磁瓦表面缺陷的准确率可达95%.

改进拉普拉斯能量和的尖锐频率局部化Contourlet域多聚焦图像融合方法

为了克服Contourlet融合在远离支撑区间上出现的混叠成分,抑制融合图像在奇异处产生伪吉布斯现象,提出了改进拉普拉斯能量和的尖锐频率局部化Contourlet(SFLCT)域多聚焦图像融合方法。采用SFLCT而不是原始的Cont-ourlet对多聚焦图像进行分解,并将多聚焦图像空域融合方法中评价图像清晰度的指标引入到SFLCT变换域,用拉普拉斯能量来选择变换域系数。然后,逆SFLCT重构得到融合结果。最后,采用循环平移来提高SFLCT的平移不变性,有效抑制融合图像在奇异处产生伪吉布斯现象。实验结果表明:对于多聚焦图像,所提方法比循环平移小波变换法的互信息提高了5.87%,QAB/F提高了2.70%,比循环平移Contourlet方法的互信息提高了1.77%,QAB/F提高了1.29%;视觉效果优于典型的空域分块拉普拉斯能量方法和平移不变小波变换方法。

基于contourlet模极大值的图像边缘检测

现存的基于空间域或小波变换域图像边缘检测算法只能有效检测出图像有限方向的边缘。本文提出在contourlet变换子带上搜索模极大值的图像边缘检测算法。通过适当地调节变换各尺度上的分解级数得到具有抛物线型尺度的基,另使其满足方向消失矩条件,则contourlet变换获得对图像中分片光滑边缘的最佳逼近。对原图像进行非下采样contourlet变换,然后对各子带进行阈值处理。由于contourlet变换子带不同于小波子带的全新特点,根据各方向子带代表的方向及其梯度方向,子带上各样本点与其梯度方向上相邻点比较模值即得变换系数模极大值。该算法不需经过复杂的连接模极大值点操作,亦可得到较高的边缘检测性能,较大地降低了计算复杂度。实验显示本文算法所提取的边缘更加逼近图像真实边缘,且具有较低的计算复杂度。