搜索资源列表
Thresh_Proceesing
- ConvertGrayToWhiteBlack函数采用硬阈值的方法，实现将图像二值化的功能。-ConvertGrayToWhiteBlack function using hard-threshold approach to realize the image binarization function.
Ban-denoising
- 本代码是基于Bandelet的硬阈值SAR图像去噪，结果与小波变换进行对比，结果表示其在峰值信噪比，边缘保持系数上均有一定的提高。压缩包中有程序和SAR图像。-This code is based on Bandelet SAR image denoising with a hard threshold .the results were compared with the wavelet transform, and the results indicated that in the peak
EMDdenoising
- 利用emd对一维信号进行去噪的最新程序。包含了三种emd去噪方法：1、直接使用小波阈值，进行硬阈值去噪；2、使用具有emd分解特性的阈值去噪；3、emd分解后的平移不变去噪。-Using emd on one-dimensional signal de-noising of the latest procedures. Emd contains three kinds of de-noising methods: a direct use of wavelet thresholding to c
dccs_main
- 基于contourlet几何信息的图像去噪程序，该程序包括小波硬阈值方法，contourlet硬阈值方法，小波和循环平移结合的方法，contourlet和循环平移结合的方法，还有本文方法。-Based on geometric information contourlet denoising program that includes wavelet hard threshold method, contourlet hard threshold method, wavelet and recy
matlab4
- 阈值法区造程序，包括软阈值和硬阈值，还有软硬这种法去噪的程序-Threshold area making process, including the soft threshold and hard threshold, as well as soft and hard process of this method de-noising
matlab5
- 主要简述了小波分析中的软阈值和硬阈值的曲线图，等等之类的很简单的程序-Wavelet analysis of the main outlines of the soft threshold and hard threshold curve, and so like a very simple program
12
- 基于小波变换的图像硬阈值去噪的源程序算法-Image based on wavelet transform of the source hard threshold denoising algorithm
image_wave_den
- 利用matlab编程，比较软阈值和硬阈值收缩方案的去噪效果。通过了Matlab2009a的测试。-Using matlab programming, comparing the denoising shrinkage results of two methods between soft threshold and hard threshold program. Testing by Matlab2009a.
testdenoise
- 基于UWT变换的去噪，利用的是硬阈值来设置阈值门限的-UWT transform based denoising, using the hard threshold to set the threshold value threshold
Untitled1
- 半阈值法小波去噪，并与硬阈值和软阈值的结果进行比较-Semi-threshold wavelet denoising, and with the hard threshold and soft threshold to compare the results of
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.
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
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
xiaoboyuzhiquzao
- 小波阈值降噪的matlab代码 里面有两个程序 一个比较了软硬阈值去噪 另外一个对多层小波分解的阈值去噪做了对比-Wavelet threshold denoising matlab code there are two programs a relatively soft and hard thresholding is another multi-layer wavelet threshold denoising to do a comparison
lift97mlhard
- 个人编写的提升格式97小波去噪，采用硬阈值法，三层分解，包括小波变换，去噪，逆变换三步-Written format of 97 individuals to enhance the wavelet denoising using hard threshold method, three decomposition, including wavelet transforms, denoising, inverse transform three-step
L0330
- 小波去噪算法，包含多种小波去噪去噪，强制去噪、软阈值去噪、硬阈值去噪-Wavelet denoising algorithm that contains a variety of wavelet denoising denoising forced denoising, soft thresholding and hard threshold denoising
Chapter_03_Demo_Greedy
- 压缩采样中各种贪婪算法性能的matlab仿真比较，包括LS-MP，OMP，WMP，硬阈算法-Compression of various sampling greedy algorithm performance of matlab simulation comparison, including LS-MP, OMP, WMP, hard threshold algorithm
Greedy
- 压缩采样中各种贪婪算法性能的matlab仿真比较，包括LS-MP，OMP，WMP，硬阈算法。-Compression of various sampling greedy algorithm performance of matlab simulation comparison, including LS-MP, OMP, WMP, hard threshold algorithm
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.