计算机工程与应用 ›› 2009, Vol. 45 ›› Issue (22): 150-152.DOI: 10.3778/j.issn.1002-8331.2009.22.049

• 图形、图像、模式识别 • 上一篇    下一篇

基于支持向量机的椒盐噪声去除方法

杨朝辉1,2,陈映鹰1   

  1. 1.同济大学 遥感与空间信息技术研究中心,上海 200092
    2.苏州科技学院 环境科学与工程学院,江苏 苏州 215011
  • 收稿日期:2008-04-28 修回日期:2008-07-23 出版日期:2009-08-01 发布日期:2009-08-01
  • 通讯作者: 杨朝辉

SVM-based approach for removing salt-pepper noise from images

YANG Zhao-hui 1,2,CHEN Ying-ying1   

  1. 1.Research Center for Remote Sensing and Spatial Information Technology,Tongji University,Shanghai 200092,China
    2.School of Environmental Science and Engineering,Suzhou University of Science and Technology,Suzhou,Jiangsu 215011,China
  • Received:2008-04-28 Revised:2008-07-23 Online:2009-08-01 Published:2009-08-01
  • Contact: YANG Zhao-hui

摘要: 针对自然图像中相邻像素的相关性及其椒盐噪声的特点,提出了一种基于支持向量机的椒盐噪声消除方法。该方法应用支持向量机的学习机制对图像灰度曲面进行最佳拟合,并从训练样本中提取支持向量与相应的决策函数,最后根据决策函数在拟合曲面上进行噪声像素点的灰度值预测,从而恢复噪声点的原始信号。通过与传统的中值滤波和均值滤波进行实验对比,提出的方法可有效地去除椒盐噪声,同时最大限度地保留图像的细节信息,尤其对高密度椒盐噪声图像的处理效果更为理想。

关键词: 椒盐噪声, 中值滤波, 支持向量机, 回归

Abstract: In view of the correlation of neighboring pixels and characteristic of salt-pepper noise in nature images,a SVM(support vector machine)-based method is proposed for restore images corrupted by salt-pepper noise.Firstly,gray surface of image is optimally fitted by the learning mechanism of SVM.Then the support vectors are extracted from the training samples and decision function is built up as a training result.Accordingly,original intensity values of noise pixels are predicted using well-fitted gray surface.Compared with traditional median filters and average filters,the approach can remove noise efficiently while preserving the more detail information,especially for those images with high noise ratio.

Key words: salt-pepper noise, median filter, support vector machine, regression