计算机应用 ›› 2010, Vol. 30 ›› Issue (8): 2080-2084.

• 图形图像处理 • 上一篇    下一篇

基于空间自适应Bayesian缩减的NSCT域图像去噪方法

孙强1,高勇2,焦李成2   

  1. 1. 西安理工大学
    2.
  • 收稿日期:2010-02-02 修回日期:2010-03-19 发布日期:2010-07-30 出版日期:2010-08-01
  • 通讯作者: 孙强
  • 基金资助:
    陕西省教育厅专项科研计划项目;西安理工大学博士科研启动基金项目

Image denoising based on spatially adaptive Bayesian shrinkage in NSCT domain

  • Received:2010-02-02 Revised:2010-03-19 Online:2010-07-30 Published:2010-08-01
  • Contact: SUN Qiang

摘要: 提出了一种基于空间自适应Bayesian缩减的NSCT域图像去噪方法。该方法运用了广义高斯分布对NSCT域图像的子带系数进行建模,并通过构造各向异性的椭圆窗口来描述各个子带内系数的局部背景特性,从而建立了NSCT域空间自适应Bayesian缩减机制的图像去噪方法。通过图像去噪实验验证了所提出方法的有效性。同时,与4种具有平移不变性的Contourlet去噪方法做了对比,进一步证实了所提出方法的优良去噪性能。

关键词: 图像去噪, 非下采样Contourlet变换, 平移不变性, 空间自适应缩减, 各向异性窗口

Abstract: A new image denoising method via spatially adaptive Bayesian shrinkage in the Nonsubsampled Contourlet Transform (NSCT) domain was propsed in this paper. the generalized Gaussian distribution was utilized to model the statistics of individual NSCT domain detail subbands. Anisotropic elliptic windows were then constructed and applied to the description of the local contextual characteristics of each coefficient located at a certain detail subband. As a result, an NSCT domain image denosing approach with spatially adaptive Bayesian shrinkage mechanism was established. The experimental results demonstrate the effectiveness of the proposed denoising method. Compared with four shiftinvariant contourletrelated denoising methods, the proposed method provides preferable denoised results.

Key words: image denoising, Nonsubsampled Contourlet Transform (NSCT), shift invariance, spatially adaptive shrinkage, anisotropic window