计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2009年
22期
150-152
,共3页
椒盐噪声%中值滤波%支持向量机%回归
椒鹽譟聲%中值濾波%支持嚮量機%迴歸
초염조성%중치려파%지지향량궤%회귀
salt-pepper noise%median filter%support vector machine%regression
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.
針對自然圖像中相鄰像素的相關性及其椒鹽譟聲的特點,提齣瞭一種基于支持嚮量機的椒鹽譟聲消除方法.該方法應用支持嚮量機的學習機製對圖像灰度麯麵進行最佳擬閤,併從訓練樣本中提取支持嚮量與相應的決策函數,最後根據決策函數在擬閤麯麵上進行譟聲像素點的灰度值預測,從而恢複譟聲點的原始信號.通過與傳統的中值濾波和均值濾波進行實驗對比,提齣的方法可有效地去除椒鹽譟聲,同時最大限度地保留圖像的細節信息,尤其對高密度椒鹽譟聲圖像的處理效果更為理想.
침대자연도상중상린상소적상관성급기초염조성적특점,제출료일충기우지지향량궤적초염조성소제방법.해방법응용지지향량궤적학습궤제대도상회도곡면진행최가의합,병종훈련양본중제취지지향량여상응적결책함수,최후근거결책함수재의합곡면상진행조성상소점적회도치예측,종이회복조성점적원시신호.통과여전통적중치려파화균치려파진행실험대비,제출적방법가유효지거제초염조성,동시최대한도지보류도상적세절신식,우기대고밀도초염조성도상적처리효과경위이상.
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.
