小波域Wiener滤波和Perona-Malik融合去噪的新算法

摘要

Aiming at the problems that the image denoising effect is poor by the traditional wavelet domain Wiener filter,and there are many pseudo Gibbs,this paper proposes a new denoising algorithm of wavelet domain Wener filter and Perona-Malik fusion.Firstly,the thermal diffusion iteration of the partial differential equation is simulated by wavelet-domain Wiener filter to remove noise,then the result obtained through the above will be done the second de-noising with the Perona- Malik algorithm.During the iterative process,the effective information of the image is pre-served by the adaptivity of the noise weight coefficient η as possible as it can.The results of simulation experiment and the analysis from comparison with other algorithms indicate that this algorithm has a good ability of denoising and suppressing the pseudo Gibbs,preserves the edge details of the image and improves the PSNR(peak signal to noise ratio)of the image.%针对传统的小波域维纳滤波图像降噪效果不理想,并产生伪Gibbs效应的问题,提出了一种小波维纳滤波和Perona-Malik融合去噪的新算法.该算法首先采用模拟偏微分方程的热扩散迭代,在小波域上进行维纳滤波去噪,由此得到的中间结果再通过Perona-Malik算法进行二次去噪,并在迭代过程中通过噪声权系数η的自适应性,在去噪过程中最大程度地保留图像的有效信息.仿真实验结果及与其他算法的对比分析表明,该算法具有较好的去噪和抑制伪Gibbs效应的能力,有效保存了图像的边缘细节,同时也提高了峰值信噪比(peak signal to noise ratio,PSNR).