基于CL多小波的语音信号去噪

本文重点分析并设计了多小波预处理及多小波的分解重构办法，并提出一种基于CL多小波的带噪语音信号去噪方法，实验证明该方法可以有效的去除语音中的噪声，同时，能较好的保留视听效果。     

平移不变小波包去噪方法

由于小波基缺乏平移不变性,传统小波及小波包去噪算法可能使信号急剧变化部分产生人为振荡现象.提出了基于平移不变的小波包去噪方法,对所分析的信号进行循环平移,利用软或硬阀值对该信号的小波包系数进行压缩,重构信号,再进行相反的循环平移,通过多次的平移—消噪—平移,平均所获得的结果,从而消除小波包基的平移依赖性.对比普通小波包去噪,该方法能有效地消除人为振荡现象,使去噪后的信号更光滑,更逼近真实信号.     

基于小波模极大值的木材近红外光谱去噪

将光谱一阶导数与小波变换相结合,对杉木木材近红外光谱数据进行预处理,采用db3小波对光谱数据进行4尺度分解,在各分解尺度上根据信号和噪声的不同传播特性.保留信号的模极大值,去除噪声的模极大值.结果表明:光谱导数 小波变换模极大值能够有效消除光谱噪声和各种因素干扰,并很好保留了光谱信号特征,使光谱信噪比有了较大提高.     

Hilbert-Huang transform and denoising for partial discharge ultrsonic singals of large motor

Empirical mode decomposition (EMD) algorithm is introduded as the core of the Hilbert-huang transform (HHT), and implementation process of EMD is analyzed. Then data compression denoising algorithm based on EMD is proposed, simulation and experimental signals are used for verification of the effect of EMD. In the same data sources, the comparison of data compression denoising approaches based on the EMD, db2 wavelet and db8 wavelet are conducted. In addition, physical experiment of the same analysis and comparisons are conducted on a running motor in a Chongqing electrical plant. Simulation and experimental results show that data compression denoising algorithm based on EMD can achieve the same denoising effect, or even better than based on db2 wavelet, db8 wavelet. The former is more perfect than the latter in the real signal processing, and denoising based on EMD is not loss of the original signal energy.     

基于TOF深度传感的植物三维点云数据获取与去噪方法

为提高植物三维重建的精度,更好地实现植物数字化研究,提出了基于TOF(time of flight)深度传感的植物三维点云数据获取与去噪方法。首先通过TOF深度传感来获取植物点云数据,采用直通滤波器对点云数据进行预处理,减少背景噪声;其次采用改进密度分析的离群点去噪算法,该算法通过结合邻近点平均距离和邻域点数数量2个特征参数,对点云数据中的离群点噪声进行检测和去除;最后采用双边滤波算法对点云内部的小尺寸噪声进行检测和去除。以番茄植株进行相关试验,试验结果表明:与传统双边滤波算法比较,该文算法最大误差降低了11.2%,平均误差降低了23.2%;与拉普拉斯滤波算法比较,最大误差降低了20.6%,平均误差降低了39.2%,表明该文提出的算法在保持点云特征的情况下,能简单高效地去除植物三维点云数据中的不同尺度噪声。     

The switching median filter denoising algorithm for encrypted images

Sometimes it is necessary to send the image data to the third party device to process. To ensure the image safety, it is necessary to encrypt the image before processing. A new algorithm for encrypted image processing is proposed. The image owner encrypts     

Research on Anti-noise Processing Method of Production Signal Based on Ensemble Empirical Mode Decomposition(EEMD)

The grain production prediction is one of the most important links in precision agriculture. In the process of grain production prediction, mechanical noise caused by the factors of difference in field topography and mechanical vibration will be mixed in the original signal, which undoubtedly will affect the prediction accuracy. Therefore, in order to reduce the influence of vibration noise on the prediction accuracy, an adaptive Ensemble Empirical Mode Decomposition (EEMD) threshold filtering algorithm was applied to the original signal in this paper: the output signal was decomposed into a finite number of Intrinsic Mode Functions (IMF) from high frequency to low frequency by using the Empirical Mode Decomposition (EMD) algorithm which could effectively restrain the mode mixing phenomenon; then the demarcation point of high and low frequency IMF components were determined by Continuous Mean Square Error criterion (CMSE), the high frequency IMF components were denoised by wavelet threshold algorithm, and finally the signal was reconstructed. The algorithm was an improved algorithm based on the commonly used wavelet threshold. The two algorithms were used to denoise the original production signal respectively, the adaptive EEMD threshold filtering algorithm had significant advantages in three denoising performance indexes of signal denoising ratio, root mean square error and smoothness. The five field verification tests showed that the average error of field experiment was 1.994% and the maximum relative error was less than 3%. According to the test results, the relative error of the predicted yield per hectare was 2.97%, which was relative to the actual yield. The test results showed that the algorithm could effectively resist noise and improve the accuracy of prediction.     

基于块排序的降噪方法及其在农业图像中的应用

农业图像采集过程中,环境因素常会带来噪声干扰,图像噪声又会对最终信息的分析结果带来影响,因此降噪对提高农业图像处理质量具有重要意义。基于块排序的非局部均值算法是一种有效的图像降噪方法,但是存在处理时间长,对大图像的处理内存要求高等问题。提出了分块优化方法,首先对大图像进行了适应于图像纹理丰富度的图像分块研究,然后分别对每个图像块进行处理。针对处理后的图像块再组合引起的边界效应,采用图像延拓的方法,有效地消除了边界影响,提高了图像降噪效果。实验结果表明,对于一般的硬件设备,改进的块排序非局部均值降噪算法能够快速处理农业中常用的图像。对于尺寸大小为512像素×512像素图像,当噪声标准偏差为50,分块数为16时,改进后的块排序降噪方法能够有效处理噪声图像。分块数为64时的处理速度是分块数为16时的1.89倍。     

基于Meyer窗函数剪切波变换的蝗虫切片图像去噪

蝗虫显微切片图像纹理边缘细节丰富,在图像获取、增强等预处理过程中经常会受到外部噪声的干扰,因此针对蝗虫切片图像去噪同时并保留纹理边缘细节的探索是研究不同蝗虫种类细胞构造的基础。基于张量的传统二维小波因其滤波器各向同性,只能表示水平和垂直两个方向,在去噪的同时会把图像中边缘纹理等细节模糊,而剪切波构造的滤波器各向异性,能够表示多个方向,这些优点使得剪切波可以有效地处理高维数据,在逼近奇异曲线时达到最优逼近。本文提出的基于Meyer窗函数的剪切波算法可以识别出图像边缘和纹理,并在去噪的同时保留纹理,以Meyer小波作为剪切波基函数,利用Meyer小波函数和尺度函数构造窗函数,然后采用Meyer窗函数来建立各向异性剪切波滤波器,再利用该剪切波滤波器对蝗虫切片图像进行多尺度分析,经过剪切波变换获得剪切波系数,最后应用硬阈值方法去除蝗虫切片图像噪声系数,经过逆变换得到蝗虫切片去噪图像。采用经典图像质量评价指标均方误差（MSE）、峰值性噪比（PSNR）、结构相似度（SSIM）对本文算法去噪性能进行评价,在噪声标准差等于30时,将本文算法与Meyer小波、偏微分方程等去噪方法进行比较,其中PSNR比Meyer小波提高2.5dB左右,比偏微分方程算法的PSNR提高2dB左右。仿真试验结果表明,本文算法去噪后的蝗虫切片图像去噪效果明显优于其他传统去噪算法,去噪结果在视觉效果上也优于其他传统去噪算法。     

砂糖橘可溶性总糖可见—近红外光谱无损检测

在波长450-2500 nm范围提取189个砂糖橘的漫反射光谱,使用sym8小波的3层分解对其进行去噪预处理,引入连续投影算法(SPA)对光谱进行压缩,从2051个波长中初步提取14个优选波长,以这14个波长建立的多元线性回归模型(MLR)的预测相关系数为0.8855,预测均方根误差为0.5111,效果优于全谱偏最小二...