摘要
研究发现,图像经过Contourlet变换之后,其变换系数在尺度内和尺度间都表现出很强的相关特性。基于此,首先根据Contourlet系数在同一尺度内的邻域相关特性,构造得到一个自适应阈值,然后在采用阈值法进行自适应阈值去噪的同时,利用Contourlet系数在相邻尺度间的相关性对系数进行进一步的取舍,从而提出一种新的基于Contourlet变换系数特性的自适应阈值图像去噪算法。该算法不仅可以有效去除噪声,而且可以很好地保留图像边缘信息。实验结果表明,在相同条件下该算法的主客观去噪效果均优于现有同类方法。
The contourlet coefficients corresponding to an image have strong correlations both at the same scale and between neighboring scales. Therefore, an adaptive threshold is firstly constructed by the intra-scale correlation property of contourlet coefficients. After the adaptive thresholding denoising, the inter-scale correlation of the contourlet coefficients is utilized to decide what processed coefficients to use. Based on the two above correlation properties of contourlet coefficients, a new adaptive contourlet-domain image deniosing algorithm is thus presented. The new algorithm cannot only remove noise thoroughly, but maintain the edges of the image effectively. Experimental results show that the proposed algorithm can get better denoising results and is superior to the available methods both visually and objectively.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2009年第2期357-361,共5页
Acta Optica Sinica
基金
国家自然科学基金(10778724)
北京交通大学科技基金(2005SM011)资助项目
作者简介
杨帆（1983-），女，硕士研究生，主要从事图像处理方面的研究。E—mail：06120410@bjtu．edu．cn
导师简介：赵瑞珍（1975-），男，博士，副教授，主要从事图像处理与小波变换等方面的研究。E-mail：rzhzhao@bjtu．edu．cn
