计算机应用 ›› 2013, Vol. 33 ›› Issue (09): 2603-2605.DOI: 10.11772/j.issn.1001-9081.2013.09.2603

• 多媒体处理技术 • 上一篇    下一篇

基于变换域的条带噪声去除方法

刘召海,杨文柱,张辰   

  1. 河北大学 数学与计算机学院,保定 071002
  • 收稿日期:2013-03-22 修回日期:2013-05-10 出版日期:2013-09-01 发布日期:2013-10-18
  • 通讯作者: 杨文柱
  • 作者简介:刘召海(1988-),男,山东临沂人,硕士研究生,主要研究方向:机器视觉;
    杨文柱(1968-),男,河北保定人,教授,博士,主要研究方向:机器视觉、人工智能;
    张辰(1988-),男,河北沧州人,硕士研究生,主要研究方向:机器视觉。
  • 基金资助:

    国际合作专项;国家科技支撑计划项目;河北省科技支撑计划项目;河北省教育厅资助项目;河北大学人才基金资助项目

Destriping method based on transform domain

LIU Haizhao,YANG Wenzhu,ZHANG Chen   

  1. College of Mathematics and Computer Science, Heber University, Baoding Hebei 071002,China
  • Received:2013-03-22 Revised:2013-05-10 Online:2013-10-18 Published:2013-09-01
  • Contact: YANG Wenzhu

摘要: 为解决线扫描图像中的条带噪声干扰问题,提出了傅里叶变换与小波分解相结合的变换域条带噪声去除方法。首先对图像进行多尺度小波分解,将包含条带噪声的小波子带与包含图像信息的小波子带分离;然后对含有条带噪声的小波子带进行傅里叶变换,并对变换系数进行带阻滤波以消除条带噪声。利用实际采集的带有条带噪声的棉花异性纤维图像进行仿真实验,结果表明:傅里叶变换与小波分解相结合的方法,去噪效果明显优于单独使用傅里叶变换或小波分解的方法,既能有效地去除图像中的条带噪声,又能较好地保持图像的细节信息。

关键词: 条带噪声, 变换域, 傅里叶变换, 小波变换, 小波阈值

Abstract: To remove the stripe noise from the line scan images, a transform domain destriping method which combined Fourier transform and wavelet decomposition was proposed. Firstly, the image was decomposed using multi-resolution wavelet decomposition to separate the subband which contained the stripe noise from other subbands. Then the subband that contained stripe noise was transformed into Fourier coefficients. The Fourier coefficients were processed by a band-stop filter to remove the stripe noise. The live collected cotton foreign fiber images with stripe noise were used in the simulation experiment. The experimental results indicate that the proposed approach which combined Fourier transform with wavelet decomposition can effectively remove the stripe noise from the image while preserving the characteristics of the original image. It gets better destriping effect than just using Fourier transform or wavelet decomposition separately.

Key words: stripe noise, transform domain, Fourier transform, wavelet transform, wavelet coefficient threshold

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