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一种新的非下采样Contourlet域图像去噪算法
  • 摘要

    作为新型高维奇异性分析工具,非下采样轮廓(Nonsubsampled Contourlet)变换不仅克服了小波(Wavelet)变换的非奇异性最优基缺点,而且提供了优于轮廓(Contourlet)变换的平移不变性.以性能优越的非下采样轮廓变换为基础,提出了一种新的图像去噪方法.该方法首先对图像进行非下采样轮廓变换,以得到不同尺度、不同方向上的变换系数;然后结合噪声分布特点确定多尺度阈值,并依此阚值对高频系数进行去噪处理;最后对去噪处理后的变换系数进行反变换,以得到去噪图像.仿真实验结果表明,该方法不仅拥有较强的抑制噪声的能力,而且具有较好的边缘保护能力,同时消除了图像边缘附近的伪吉布斯(Gibbs)现象,整体性能优于小波变换图像去噪和轮廓变换图像去噪方法.

  • 作者

    付仲凯  王向阳  郑宏亮  FU Zhong-kai  WANG Xiang-yang  ZHENG Hong-liang 

  • 作者单位

    辽宁师范大学计算机与信息技术学院,大连,116029/辽宁师范大学计算机与信息技术学院,大连,116029;苏州大学江苏省计算机信息处理技术重点实验室,苏州,215006

  • 刊期

    2009年11期 ISTIC PKU

  • 关键词

    图像去噪  非下采样轮廓变换  多尺度阚值  伪吉布斯现象  Image denoising  Nonsubsampled contourlet transform  Multi-scale threshold  Gibbs 

参考文献
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