Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (6): 166-168.DOI: 10.3778/j.issn.1002-8331.2010.06.048

• 图形、图像、模式识别 • Previous Articles     Next Articles

New image denoising method based on multi-scale decomposition and nonlinear energy preserve

FAN Yan1,SONG Xiao-ning2,WU Xiao-jun3   

  1. 1.School of Electronics and Information,Jiangsu University of Science and Technology,Zhenjiang,Jiangsu 212003,China
    2.Department of Computer Science and Technology,Nanjing University of Science and Technology,Nanjing 210094,China
    3.School of Information Engineering,Jiangnan University,Wuxi,Jiangsu 214122,China
  • Received:2008-08-29 Revised:2008-11-17 Online:2010-02-21 Published:2010-02-21
  • Contact: FAN Yan

多尺度域非线性能量保持的图像去噪新方法

范 燕1,宋晓宁2,吴小俊3   

  1. 1.江苏科技大学 电子信息学院,江苏 镇江 212003
    2.南京理工大学 计算机科学与技术系,南京 210094
    3.江南大学 信息工程学院,江苏 无锡 214122
  • 通讯作者: 范 燕

Abstract: 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.

Key words: Contourlet transform, kernel principal component analysis, image denoising, multi-scale decomposition

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。

关键词: 轮廓波变换, 核主成分分析, 图像去噪, 多尺度分解

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