基于小波收缩阈值和维纳滤波的去噪方法

Denoising method with wavelet shrinkage adaptive thresholding and wiener filter

针对高斯噪声图像的结构特点及传统去噪方法中所存在的问题,提出一种基于小波收缩阈值法和维纳滤波法相结合的图像去噪方法。采用小波收缩阈值法对图像进行去噪,对处理后的图像用维纳滤波法进行平滑处理。采用独立自适应阈值,对其子带阈值进行确定,并引入调节系数。仿真结果表明,所提出的方法在高斯去噪效果和保留图像细节信息性能方面优于中值滤波算法、均值滤波算法等方法。

In accordance with the problem of Gaussian noise and the traditional image of the structural features in the image denoising,a denoise method is proposed based on wavelet shrinkage adaptive thresholding and wiener filter.Firstly,the image denoise is carried out by using wavelet shrinkage adaptive thresholding,then the smoothing processing is carried out for the processed image with the Wiener filtering method.The sub-band threshold of independent adaptive threshold is determined with the adjustment coefficient.Experiments show the proposed method is more efficient on the Gaussian-noise denoising and retaining image details than that of standard median filter and mean filter algorithm.