小波变换用于去除高频随机噪声

High-frequency random noise elimination using wavelet transform

小波变换以小波奇性分析中得出的一些结论作为理论依据，利用连续小波变换情况下信号与噪声呈现出的不同性质来确定信噪比较低的部分，去掉相应的正交小波分量，再经反变换后便可达到压制噪声的目的。小波变换用于去除高频随机噪声方法的主要特点是：可以自动地判定低信噪比区间，且无论在时间域或频率域均可局部地进行去除噪声。数值试验的结果表明：经本文方法处理的剖面，平均信噪比、视觉信噪比以及视觉分辨率均可得到改善。

Wavelet transform is theoretically based on some conclusions which are derived from wavelet singularity analysis. By analysing the very different characteristics of signals and noises in running wavelet transforms,we can locate low S/N contents, remove the corresponding orthogonal wavelet component and achieve noise elimination after inverse transform. High-frequency random noise elimination using wavelet transform is characterized by automatically locating the low S/N intervals,and local noise eliminations in both time domain and frequency domain. Numerical experiment results show that the method produces the seismic section which shows improved effects in average signal/noise ratio,visual signal/noise ratio and visual resolution．