小波域Wiener滤波和Perona-Malik融合去噪的新算法  

New Denoising Algorithm of Wavelet-Domain Wiener Filter and Perona-Malik Fusion

作　　者：尹芳[1,2] 付自如 于晓洋[2] YIN Fang;FU Ziru;YU Xiaoyang(School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China;Instrument Science and Technology Postdoctoral Research Station, Harbin University of Science and Technology,Harbin 150080, China)

机构地区：[1]哈尔滨理工大学计算机科学与技术学院,哈尔滨150080 [2]哈尔滨理工大学仪器科学与技术博士后科研流动站,哈尔滨150080

出　　处：《计算机科学与探索》2018年第3期432-441,共10页Journal of Frontiers of Computer Science and Technology

基　　金：国家自然科学基金;No.61440025;黑龙江省教育厅科学技术研究项目;No.12541119~~

摘　　要：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)。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 denoising with the Perona-Malik algorithm.During the iterative process,the effective information of the image is preserved by the adaptivity of the noise weight coefficientηas 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.

关 键 词：小波域 领域边界 维纳滤波 Perona-Malik 伪Gibbs 

分 类 号：TP317.4[自动化与计算机技术—计算机软件与理论]