摘要
提出了一种基于Contourlet多尺度分解域核主成分分析的图像去噪新方法。该方法首先对源图像进行Contourlet分解,在不同频段的子带图像中,利用核主成分分析方法进行能量保持,用重构图像来进行子带去噪,最后通过Contourlet逆变换得到去噪之后的图像。实验结果表明,该方法不仅有效地降低了图像噪声,且峰值信噪比也较高。
A new method based on Kernel Principal Component Analysis(KPCA) in contourlet multi-scale decomposition domain is proposed in order to solve image denoising problem.Firstly,contourlet transformation is applied on source image with this method.Secondly,image denoising is executed on different frequency sub-images with reconstructed images by KPCA method.Finally,denoised image is attained with inverse contourlet transform.The experimental results on images demonstrate that the proposed method not only decrease image noise effectively,but also improve PSNR.
出处
《计算机工程与应用》
CSCD
北大核心
2010年第6期166-168,230,共4页
Computer Engineering and Applications
基金
国家自然科学基金(No.60572034)
江苏省自然科学基金(No. BK2004058)
江苏科技大学电子信息学院青年教师科研立项资助~~
关键词
轮廓波变换
核主成分分析
图像去噪
多尺度分解
Contourlet transform
kernel principal component analysis
image denoising
mnlti-scale decomposition