基于静态小波分解的机加工表面图象滤波

Multiscale filtering of machined surface image based on static wavelet decomposition

由于在机械加工过程中机械振动和噪声回波的相干性 ,机加工表面图象上会存在白噪声 ,为了削弱这些噪声的影响 ,提出了一种基于静态小波分解的自适应阈值滤波方法 ,该方法首先将机加工图象分解至静态小波域 ,然后在静态小波域中将噪声的小波系数收缩至零 ,将此算法应用于机加工图象噪声滤波 ,并与基于Mallat分解的滤波算法和另外三种典型图象滤波算法进行比较 ,结果表明 ,该方法不仅可以有效的去除噪声 ,而且还可以保持图象的精密纹理结构。

Machined surface images are disturbed by white noise due to vibration and relativity of noise echo during the machine process. In order to weaken the influence of noise, we propose a new self- reliant threshold filtering based on static wavelet decomposition, i.e. decomposing the image into the static wavelet field at first, then suppressing the wavelet coefficients of noise to zero. The method is used in the noise faltering of a machined surface image and is compared with the filtering method based on Mallat decomposition and three other classical image filtering methods. The result shows that the presented approach has the advantages of both eliminating noise and preserving nicety texture of image.