摘要
为了克服传统去图像噪声算法的限制,该文提出一种基于非降采样(Nonsubsampled)Contourlet变换的增强新算法(NNIEM-NSCT)。此新算法通过充分利用方向子带相关性的自适应贝叶斯阈值,既保护了图像边缘细节,又可更好地抑制图像噪声。其次,文中构造的非线性增强匹配函数,通过改变变换域的系数能有效对图像强弱边缘进行不同程度的增强。实验结果证明,该文新算法在图像细节处理上,优于基于NSCT的方法,细节方差(DV)大约为NSCT的2倍,背景方差(BV)基本保持不变,并且具有更好的视觉效果。
To overcome the limitation of traditional image denoising algorithms, a Novel Nonlinear Image Enhancement Method based on NonSubsampled Contourlet Tansform （NNIEM-NSCT） is proposed in this paper. The method not only preserves the details of image edge but also suppresses the noise by means of an adaptive Bayes shrinkage threshold which takes advantages of the relationship across all subbands in the same level. Secondly, a nonlinear enhancement mapping function is constructed by modifying the NSCT coefficients to change the image enhancement of weak and strong edges efficiently. Experimental results show that the proposed method outperforms the NSCT-based method. The Detail Variance （DV） of proposed method improves about two times more than that of NSCT-based method, when the Background Variance （BV） is almost equal, and a better visual effect is gotten.
出处
《电子与信息学报》
EI
CSCD
北大核心
2009年第8期1786-1790,共5页
Journal of Electronics & Information Technology
