applies soft

利用小波分析中的小波抑制对信号进行处理，出去噪声和振动对信号的影响，得到源信号。-The use of wavelet analysis wavelet inhibition of signal processing, the signal out of noise and vibration on the impact to be the source signal.

这是一组二维小波变换的经典程序，对研究小波的很有用，里边有对应说明-This is a group of two-dimensional wavelet transform of the classical procedures, the study of wavelets useful, inside there is the corresponding description

具有纠错能力的小波图像混合编码，很值得参考-With error correction ability of the mixed wavelet image coding, it is worth considering

小波网络matlab源程序 转载自研学论坛-wavelet network reproduced from the source Matlab Institute Science Forum

关于二维小波变换的程序 [精华] 说明：此算法重在概念，速度并不是很快。因为FOR循环的缘故。此程序从循环矩阵的观点出发，把圆周卷积和快速幅里叶变换建立了联系。实现了分解和无失真重构。它只做了一层分解，即将256x256图形分解成为64x64的四个图形，避免了使用WKEEP()的困惑。主要思想为用小波滤波器族构造正交阵W,变换写为B=W*A*W ,反变换为：A=W *A*W,这与所有正交变换无异。W为循环正交矩阵，因此可用FFT实现快速运算，难点就在重构矩阵上。若用矩阵概念明确，一个共扼转置可以搞顶。但FFT的使用必须找到与分解序列的关系。-on Wavelet Transform procedures [best] Note : This algorithm important concept is not speed quickly. Because FOR cycle nowadays. This program cycle from the point of view of Matrix, circular convolution and fast Fourier transform is established links. To achieve the decomposition and reconstruction without distortion. It only had one decomposition, 256x256 graphics will be 64x64 decomposition of the four graphics, avoiding the use of WKEEP () confusion. The main idea of using wavelet filter generator orthogonal array W, write to transform B = W* A* W, anti-Transform : A = W* A* W, which is orthogonal transformation all the same. W cycle orthogonal matrix, can be used to achieve rapid FFT computation, on the dif

基于小波变换模极大的多尺度图像边缘检测matlab源代码-based on wavelet transform modulus maxima of multiscale edge detection Matlab source code

任意阶daubechies小波函数的构造，包括尺度函数和小波函数以及滤波器系数的构造-arbitrary order PG wavelet function, including scaling function and wavelet function and the structure of the filter coefficients

利用matlab进行维娜虑波得分析与处理-using Matlab for consideration Wei-na-in analysis and treatment

小波与计算机图形学-wavelet and computer graphics

用小波进行地震信号低切处理的例子-seismic wavelet signal processing low-cut examples