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xiaoboquzao
- 一维信号小波去噪，运用硬、软阈值进行去噪-One-dimensional signal wavelet de-noising, the use of hard and soft threshold de-noising
About-signal-transmission
- 比较传统小波变换中的硬阈值处理、软阈值处理，与提升小波变换之间关于信号传输中去噪的效果对比-About signal transmission denoising effect of contrast between the more traditional wavelet transform hard thresholding and soft thresholding, wavelet transform and upgrade
Garrote
- Garrote法去噪，它是小波阈值去噪中的一种方法，效果优于软阈值法和硬阈值法-Garrote method denoising，It is one of the wavelet threshold denoising method, the effect is better than that of soft threshold and hard threshold method
fenceng
- 对小波信号进行去噪处理，主要采用分层重构，软硬阀值的方法进行仿真。-To deal with the noise of the wavelet signal, mainly adopts hierarchical reconstruction, hard and soft threshold method are simulated.
xiaoboquzao
- 对含噪正弦波分别强制消噪、默认消噪、给定软阈值消噪等等方法进行消噪，选取不同的小波函数、不同的阈值函数将会产生不同的效果。其中常用的阈值函数包括两种：硬阈值法和软阈值法。-With the method of noise elimination, the default de-noising and the given soft threshold denoising method, the different wavelet function and the threshold functio
xiaoboyuzhi
- 各种方法的去噪程序 den1.m 使用半软阈值方法对图像进行去噪 den1_5_1.m 半软阈值的改进方法 对第一层重构图像进行均值滤波 den1_9.m 半软阈值的改进方法 将线性衰减的函数改为指数的 den1_10.m 半软阈值的改进方法 对第一层的重构图像再次进行小波阈值去噪 den2.m 用软硬阈值函数的改进方法进行去噪 den3.m 用广义阈值函数进行去噪 den4.m 用自适应特征阈值函数进行去噪 wdenoise
wave-denoising
- 小波阈值去噪，含软阈值去噪，硬阈值去噪和改进阈值去噪算法-The wavelet threshold denoising, including soft threshold denoising, hard threshold denoising threshold denoising algorithm and improvement
xiaoboquzao
- 这是一个硬阈值、软阈值和自己改进阈值函数小波阈值去噪程序-This is a hard threshold, soft threshold and an improved wavelet threshold denoising threshold function
qz
- 用小波进行去噪，内部有软阈值去噪和硬阈值去噪-Using wavelet denoising, internal soft threshold denoising and hard threshold denoising
test
- 基于小波变换的软、硬阈值处理-针对信号进行的处理-Based on wavelet transform is soft and hard threshold processing- for signal processing
xiaoboyuzhi
- 小波变换硬软阈值，适合初学者或者深入研究小波的人群使用-Hard and soft threshold wavelet transform, for beginners or in-depth study of wavelet
kennun
- 比较了软阈值，硬阈值及当今各种阈值计算方法,插值与拟合的matlab实现，基于小波变换的数字水印算法matlab代码。- Comparison of soft threshold and hard threshold and today various threshold calculation method, Interpolation and fitting matlab implementation, Based on wavelet transform digital watermark
biepei
- 关于小波的matlab复合分析，比较了软阈值，硬阈值及当今各种阈值计算方法,使用matlab实现智能预测控制算法。- Matlab wavelet analysis on complex, Comparison of soft threshold and hard threshold and today various threshold calculation method, Use matlab intelligent predictive control algorithm.
wavelets-image-processing
- 包括小波变换去噪和边缘检测两个方面的代码，其中小波去噪代码中包含硬阈值，软阈值和半软阈值三种方法，而小波模极大值方法中包含含噪声和无噪声两种图像的边缘检测-Including the de-noising and edge detection wavelet transform two aspects of the code, where the wavelet de-noising code contains hard threshold, soft and semi-soft thresho
quzao_matlab
- 去噪为仿真信号建立； 去噪1为eemd去噪； 去噪2为形态滤波器； 去噪3为小波去噪，全局阈值，给定阈值（软阈值，硬阈值）； 去噪4为实际去噪-2 for morphological filter denoising Denoising 3 as the wavelet denoising, global threshold, the given threshold (soft threshold and hard threshold) 4 for practical
xiaoboquzao_matlab
- 基于小波变换的图像阈值去噪的matlab代码，可以实现硬阈值，软阈值以及一种自适应阈值算法并且完成他们的对比；-One kind matlab code image threshold denoising based on wavelet transform can be achieved hard threshold and soft threshold and an adaptive threshold algorithm and complete their contrast
sine-wave-denoising
- denoising noisy sine wave by hard and soft threshloding and plotting variation in every level of wavelet decomposition
haofai
- 处理信号的时频分析，采用了小波去噪的思想，比较了软阈值，硬阈值及当今各种阈值计算方法。- When processing a signal frequency analysis, Using wavelet denoising thought, Comparison of soft threshold and hard threshold and today various threshold calculation method.
bouning_v12
- 基于小波变换的数字水印算法matlab代码，比较了软阈值，硬阈值及当今各种阈值计算方法,课程设计时编写的matlab程序代码。- Based on wavelet transform digital watermarking algorithm matlab code, Comparison of soft threshold and hard threshold and today various threshold calculation method, Course designed to
qingfai_v20
- matlab小波分析程序，调试通过可以使用，比较了软阈值，硬阈值及当今各种阈值计算方法。- matlab wavelet analysis program, Debugging can be used, Comparison of soft threshold and hard threshold and today various threshold calculation method.