摘要
提出了基于混沌粒子群优化的图像Contourlet阈值去噪方法.该方法在Contourlet变换域内利用混沌粒子群算法来确定最优阈值,再通过软阈值函数去噪,且不需要噪音方差等先验信息.实验结果表明:该方法与小波Bayeshrink阈值、基于粒子群的小波阈值、Contourlet自适应阈值等去噪方法相比,能有效地去除高斯白噪音和椒盐噪音的混合噪音,提高峰值信噪比,并较好地保留图像的细节和纹理,从而明显地改善了图像的视觉效果.
A method of the image Contourlet threshold de-noising based on chaotic particle swarm optimization is proposed. This method can acquire the optimal threshold using chaotic particle swarm optimization in the Contourlet transform domain and then remove the noise by soft threshold function. It does not need the prior information of noise variance. The experimental results show that this method can effectively eliminate the mixed Gaussian white noise and Pepper Salt noise , increase the peak signal to noise ratio（PSNR） and preserve the images details and texture well compared with the de-noising methods of Bayesian wavelet threshold, wavelet threshold by particle swarm optimization and adaptive Contourlet threshold. So the proposed method can improve significantly image visual effect.
出处
《光子学报》
EI
CAS
CSCD
北大核心
2010年第9期1645-1651,共7页
Acta Photonica Sinica
基金
国家自然科学基金(60872065)资助
关键词
图像处理
阈值去噪
CONTOURLET变换
混沌粒子群
峰值信噪比
Image processing
Threshold de-noising
Contourlet transform
Chaotic particle swarm optimization
Peak signal to noise ratio（PSNR）
作者简介
Tel：025—84896490—10606 Email：gumptions@yahoo．com．cn WU Yi-quan was born in 1963. He received the Ph. D. degree from School of Information Science and Technology, Nanjing University of Aeronautics and Astronautics. Now he is a professor and his research interests focus on image processing, target detection and tracking and so on.
