光电工程, 2009, 36 (10): 111, 网络出版: 2010-01-31
基于复轮廓波变换的图像消噪
Image Denoising Based on Complex Contourlet Transform
图像消噪 复轮廓波变换 蒙特卡罗法 门限收敛因子 峰值信噪比 image denoising complex contourlet transform Mento-Carlo method threshold convergence factors PSNR
摘要
为了克服实轮廓波图像消噪后广泛存在的混叠现象,研究了基于双树复小波级联方向滤波器架构的复轮廓波变换图像消噪的若干性质,证明了对于高斯白噪声图像,该变换具有更好的分割能力和抑制能力,并在此基础上提出了一种基于该变换的图像消噪算法。该算法采用蒙特卡罗方法来确定门限收敛因子,并采用这些因子修正3σ准则,对变换域系数模值采用硬阈值处理。图像消噪实验结果表明:该消噪算法比基于实轮廓波变换的消噪算法,具有更高的峰值信噪比和更好的视觉效果。
Abstract
In order to overcome the aliasing phenomenon commonly existing in real contourlet transform image denoising, some characters of complex contourlet transform whose structure is a cascading of dual tree complex wavelet and directional filter banks are discussed. It is proved that the transform performs well at division and restraining ability under white Gaussian noise condition. Then, an image denoising algorithm was proposed based on the transform. Furthermore, Mento-Carlo method was used to find convergence factors for modifying the 3σ rule, and hard threshold method was carried on complex contourlet tranform domain coefficients. Experimental results show that the image denoising algorithm proposed in this paper is superior to that using real contourlet transform both at Peak Signal-to-noise Ratio (PSNR) values and visual quality.
陈新武, 龚俊斌, 刘玮, 田金文. 基于复轮廓波变换的图像消噪[J]. 光电工程, 2009, 36(10): 111. CHEN Xin-wu, GONG Jun-bin, LIU Wei, TIAN Jin-wen. Image Denoising Based on Complex Contourlet Transform[J]. Opto-Electronic Engineering, 2009, 36(10): 111.