EMD similars the wavelet transforms, the decomposition result is from high frequency to the low frequency distribution, namely the noise mainly concentrates in first several.
经验模式分解的主要思想类似小波变换，分解结果是由高频到低频分布的，即噪声主要集中在前几层。
For the image high-frequency part, use the new evaluation factor - wavelet neighborhood information to choose the ultimate wavelet high-frequency coefficients.
使用由方差和平均梯度构造的新的评价因子——小波邻域信息量作为融合规则选取小波高频系数。
Approximate coefficients in low frequency band of two images and the average over absolute wavelet packet coefficients of high frequency brand form a fused coefficients matrix.
对两幅图像低频波段的逼近系数取均值，对高频段选取绝对值大的小波包系数，得到融合系数矩阵。
Wavelet transform has a good analyzing feature and a good time frequency localization. Spectrum whitening is a effective tool of frequency compensation in high resolution processing.
小波变换具有分析性质好和时—频局域化好的特性，而谱白化方法是高分辨处理中一种有效的频率补偿手段。
Compared with classic wavelet technique, the proposed method can adjust not only the low frequency components of earthquake wave, but also the high frequency components.
和传统的小波分析相比，该方法不仅对地震波低频分量进行精确调整，还可对其高频成分实现类似的调整。
Network traffic is broken down into different frequency, and anomaly change of network traffic is detected through the high-frequency power analysis, that is the change of wavelet variance.
将网络流量分解到不同的频段，根据高频段频谱能量，即小波方差的变化对网络流量异常进行检测。
According to features of power quality disturbance signal, this paper detects the disturbance signals of high-frequency, transient impulse and voltage notch with wavelet transforming.
针对电能质量扰动信号的特性，本文采用小波变换对电力系统的高频、瞬时脉冲、电压切痕扰动信号进行检测。
Based on the theory of signal strangeness detect, the maxima modulus in high frequency of wavelet resolving can be used as a criterion to classify steady and non-steady state phenomena.
基于信号的奇异性检测理论，利用小波分解的高频系数是否具有模极大值作为区分稳态和非稳态现象的判据。
Finally, the initial threshold is identified and relation matrix is constructed, and the wavelet coefficients of the high frequency subbands are encoded progressively by using relation matrix.
首先确定初始阈值，并构造小波系数的关系矩阵，然后结合关系矩阵对高频子带系数进行逐次逼近量化编码。
The circular region F-norms in low frequency band of wavelet transform is used as color feature of the image and the energy ratio of high frequency band is used to form texture descriptor.
颜色特征采用整数小波低频系数矩阵中环形区域的F -范数，纹理特征则采用高频部分的能量比值。
Assisted by other bands high frequency geometric information, reconstruct noise band image with wavelet reconstruction filter to de-noise noise band image.
以其他波段的几何信息辅助噪声污染波段重构，经过相应的小波重构滤波器滤波，获得该波段图像的重建以进行消噪。
Data were embedded into the Least Significant Bit-plane (LSB) of high frequency CDF (2, 2) integer wavelet coefficients, which accorded with the principle of Human Visual System (HVS).
在图像的小波子带系数的最低位(LSB)嵌入隐藏数据，将数据嵌入到小波cdf(2,2)的高频子带系数中，符合人的视觉系统原理(HVS)。
The original motion mask image can be extracted from the high frequency frames which resulting from temporal decomposition of the original video sequence by wavelet transform.
将视频序列进行时间轴一维小波变换，利用变换后的高频帧信息提取出初步的运动掩模图像；
By using the method of wavelet packet transform on the big differential currents, extracting high-frequency coefficients of wavelet packet, to decide faults within or outside the buses.
该方法应用小波包对大差电流进行变换，提取高频小波包系数来判定区内或区外故障。
Firstly, the best wavelet basis selection was conducted via message entropy function; then the gas load was decomposed to low frequency signal and high frequency signal by two times through it.
首先用信息熵函数最小选择最优小波基，然后用其对燃气负荷进行二层分解得到负荷的低频信号和高频信号。
A method of image magnification with ENO interpolation which based on wavelet transform is presented, to enhance the coefficient of the high frequency subband.
提出了基于小波变换的ENO插值图像放大方法，对插值后的图像做小波变换，对于高频系数进行修正，经过重构保持放大图像的清晰度。
The paper proposes application of Wavelet Neural Network in high-frequency time series calendar effects' study. At last, the paper proves that WNN is better than classical FFF regression.
提出了用小波神经网络（WNN）来定量研究高频金融时间序列“日历效应”，通过比较发现WNN是比弹性傅立叶形式（FFF）回归技术更具优势的方法。
There exist some wet and high-frequency noise fingerprint in large fingerprint databases. A low quality fingerprint enhanced algorithm was designed in wavelet domain.
针对采集到的指纹库中存在指纹过湿，以及存在高频噪声的问题，提出了一种基于小波分析的方向滤波算法。
Methods the noise on high frequency domain was obtained and magnified after the pathologic voice signal was decomposed on multiple levels by means of the wavelet transform.
方法应用小波变换对早期病理嗓音信号进行多重分解，提取和放大频谱的高端噪声。
Firstly, the input image is decomposed using wavelet. Then sub-image which corresponds to high frequency in vertical direction is binarised. Finally, prior knowledge is used to locate the text region.
该算法首先对输入图像进行小波变换，在对小波变换后的垂直方向上的高频分量图进行二值化及规则限制。
The high frequency gained from wavelet transform can reflect the images' texture.
所以获得表情图像的纹理信息对表情识别是有帮助的。
Original ECG preprocessing:remove noise such as high-frequency power supply and interference factors such as baseline drift by wavelet function and filter;
原始心电信号的预处理：利用小波降噪函数和滤波器去除电源干扰的高频噪声和基线漂移及抖动等干扰因素；
Original ECG preprocessing:remove noise such as high-frequency power supply and interference factors such as baseline drift by wavelet function and filter;
原始心电信号的预处理：利用小波降噪函数和滤波器去除电源干扰的高频噪声和基线漂移及抖动等干扰因素；
应用推荐