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lunwen22
- 小波去噪的仿真研究 ：介绍了小波软门限和硬门限法上嗡的处理方法．分析了采用不同的小波进行软门限去噪的效果，比较了软门限去噪和硬门 限去噪的特点。仿真结果表叫，对高斯白噪声的上哚处理，选用c1b8小波比用symI和haar小波町以得到更好的去噪效果， 软门限法优于硬门限法-Simulation Study of Wavelet Denoising: The wavelet soft threshold and hard thresholding approach on the hum.
my_wthresh
- 软硬阈值函数和自定义的基于非线性变换阈值函数曲线图-Hard and soft threshold function and custom thresholding function based on nonlinear transformation curve
wavelet
- 使用小波变换进行图像去噪，对得到的高频部分利用软阈值和硬阈值法去噪-Image denoising using wavelet transform, the high frequency part by the use of soft threshold and hard threshold denoising
softthreshold_Bayes
- The function apply soft thresholding to wavelet coefficients at decomposition level lev calculating the threshold adapted to the various sub-bands-The function apply soft thresholding to wavelet coefficients at decomposition level lev calculating
rythreshold
- 小波阈值去噪的软硬阈值去噪算法，里面包含了软硬阈值不同的去噪效果，大家一起学习哈-Hard and soft wavelet threshold denoising threshold denoising algorithm, which includes hard and soft threshold denoising different effects, we will study together and Kazakhstan
wpdencmp
- contour函数的使用 matlab小波降噪 全局软阈值消噪图像 无偏估计软阈值消噪图像 同态滤波 启发式软阈值消噪图像 -contour matlab wavelet noise reduction function uses the global soft-threshold denoising images unbiased soft threshold denoising images with state of the heuristic filter
matlab
- 基于小波软阈值的信号去噪 小波去噪 小波分解与重构-Soft threshold based on wavelet signal denoising wavelet decomposition and reconstruction wavelet denoising
L_307
- 用matlab软件对图像惊醒压缩去噪。分为软阈值去噪硬阈值去噪等-Woke up with matlab software for image compression denoising. Divided into soft threshold denoising hard threshold denoising
thresholdHSI
- 采用小波变换的方法实现心电信号的滤波，分别使用了硬阈值、软阈值和改进阈值方法，并实现了滤波效果的评价（均方差和信噪比）-Wavelet transform of ECG signal filtering method, respectively, using a hard threshold and soft threshold and improve the threshold method, and to achieve the filtering effect of the evaluat
wavelet-image
- 二维图像信号的去噪步骤： （1）二维图像信号的小波分解。选择合适的小波与恰当的分解层次N，并对待压缩的二维图像信号进行N层分解计算。 （2）对分解后的每一层高频系数，选择一个恰当的阈值，并对该层高频系数进行软阈值量化处理。 （3）二维图像信号的小波重构。用小波分解后的第N层近似（低频系数）和经过阈值量化处理后的各层细节（高频系数），对二维信号进行小波重构。-Two-dimensional image signal denoising steps: (1) two-dimensiona
Wavelet_denoise
- 将小波分解，每层分解的系数用软域值处理，实现小波域值去噪。-Wavelet decomposition, the decomposition of each factor with a soft threshold processing, and wavelet denoising.
faboxiaozao
- 方波信号削噪，用sym8小波进行三层分解并用heursure软阈值进行小波系数阈值化-Cut a square wave signal noise, three with sym8 wavelet decomposition and use heursure soft threshold wavelet coefficients thresholding
aw2
- 基于改进半软阈值降噪法的输电线路故障测距.-Based on the improved half soft threshold denoising method in the fault location of transmission line.
softthreshold
- MATLAB小波变换软阀值法对信号去噪的源程序-MATLAB soft threshold wavelet denoising source program
wavelet_wx
- （1.）二维信号的小波分解。选择一个小波和小波分解的层次N，然后计算信号s到第N层的分解。 （2）对高斯系数进行阈值量化。对于从1到N的每一层，选择一个阈值，并对这一层的高斯系数进行软阈值量化处理。 （3）二维信号的重构。根据小波分解的第N层的低频系数和经过修改的从第1层到第N层的各层高频系数计算二维信号的小波重构。 -(1) 2 d signal wavelet decomposition. Choose a wavelet and wavelet decomposition le
0d9b94561a6b
- MATLAB编程在图像去噪中的几个应用程序。包括小波阈值去噪中的软阈值和硬阈值去噪等。-MATLAB programming several applications in image denoising. Including the soft threshold and hard threshold denoising in the wavelet threshold denoising.
mjiaqiang1
- 下面是我编的一个软硬阈值去噪的matlab程序 下面是我编的一个软硬阈值去噪的matlab程序：thr=sigma*(2*lgN)1/2/lg(j+1) 用[x,xn]=wnoise(2,10,6) 产生噪声测试数据-The following is Part I of a of hard and soft threshold denoising matlab procedure is the following hardware and software, I made a threshold
mjiaqiang2
- 下面是我编的一个软硬阈值去噪的matlab程序 下面是我编的一个软硬阈值去噪的matlab程序：thr=sigma*(2*lgN)1/2/lg(j+1) 用[x,xn]=wnoise(2,10,6) 产生噪声测试数据-The following is Part I of a of hard and soft threshold denoising matlab procedure is the following hardware and software, I made a threshold
xiaobofenjiehechougou
- 小波分解和重构 小波分解和重构 -thr=sigma*(2*lgN)1/2/lg(j+1) 用[x,xn]=wnoise(2,10,6) 产生噪声测试数据-The following is Part I of a of hard and soft threshold denoising matlab procedure is the following hardware and software, I made a threshold denoising matlab program: thr
8788730
- 小波软阈值的去噪处理代码,程序内容写在在程序的注释里。阈值的更改没有实现可视化，在源程序中可以改。-Wavelet soft threshold denoising code program written in the comments. The threshold changes were not visualized, can be changed in the source program.