【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。更多还原

【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.更多还原