thresholddenoisingbasedonwavelet

曲波变换，matlab下的曲波变换，里面也含有图像去噪的简单算法，包括硬阈值去噪等等，在matlab2012a下运行完全没有问题，请放心使用。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。

用小波变换去除荧光背景，扣除基线。matlab(Remove fluorescence background, net baseline using wavelet transform. matlab)

基于小波变换的包络分析用于轴承故障诊断的源代码(Bearing envelope analysis)

对信号进行小波去噪再重构，针对不同分解层次的高频系数，采用分层阈值方式来量化高频系数。(Signal is carried out the wavelet denoising again reconstructed to quantify high-frequency coefficients, high-frequency coefficients for different levels of decomposition, hierarchical threshold.)

常用的db，haar，墨西哥帽等小波的程序，帮助认识小波的性质(This is commonly used dB, Haar, Mexico hat and other wavelet program, can help you understand the nature of wavelet)

运行emd,得到输入信号X的固有模态函数（imf），每列代表着信号X的一阶分量，X就是我们采集的信号。 hhtdn是hht去噪的程序，可以不看，仅看emd 和 findpeaks wvtdn是wavelet去噪程序，如果只是分解信号，仅用下面的语句即可 [C,L] = wavedec(x_noise,dl,wn); x_noise就是我们采集的信号，dl是分解的阶数，wn是分解的母小波名称(% Empiricial Mode Decomposition (Hilbert-Huang Transform) % imf = emd(x) % Func : findpeaks)

this project by karen lees in 2002 is "image compression by wavelet" 7ayder..eng

实现了基于小波变化的图像融合,并将融合结果显示出来(It realize the Image fusion based on wavelet transform and show the tesult)

提升小波变换程序， %LWT Lifting wavelet decomposition 1-D. % LWT performs a 1-D lifting wavelet decomposition % with respect to a particular lifted wavelet that you specify.(lifting wavelet transform procedures% Local Walsh Transform Lifting wavelet decomposition 1-D. % Local Walsh Transform performs a 1-D lifting wavelet decomposition% with respect to a particular lifted wavelet that you specify.)

数字图像获取, 处理及实践应用源代码——很多有用的图像处理的源代码：图像显示（原图、抖动），图像增强（灰度变换、直方图均衡、多种滤波器、伪彩色增强等），图像复原（运动模糊、逆滤波等等），图像变换（傅立叶变换、快速傅立叶变换、离散余弦变换、沃尔什变换、霍特林变换、小波变换、小波反变换），图像编码（霍夫曼编码、香浓-费诺编码、算术编码、位平面编码等），图像识别。(Digital image acquisition, processing and practical application source code- a lot of useful image processing source code: Image Display (image, jitter), image enhancement (gray-scale transformation, histogram equalization, multi-filter, pseudo-color enhancement, etc. ), image restoration (motion blur, inverse filtering, etc.), image transform (Fourier transform, fast Fourier transform, discrete cosine transform, Walsh transform, Hotelling transform, wavelet transform, wavelet transform), image coding (Huffman coding, Shannon- Fenno coding, arithmetic coding, bit plane coding, etc.), image recognition.)