关键词: 多特征, 加权支持向量机, 支持向量回归机, 椒盐噪声, 图像去噪

Abstract: The authors put forward an image de-noising method by combining multiple features with weighted Support Vector Machine (SVM) based on the image de-noising by using SVM. Firstly, according to the adjacent pixels correlation in the image and the characteristics of salt-pepper noises, multiple features were extracted from noisy image. Then the noise points in the noisy image were detected by using weighted SVM classifier which improved on imbalanced dataset, then Support Vector Regression (SVR) was used to forecast the gray value of noise points, finally the image was reconstructed so as to remove noise points. The experimental results show that the proposed method can improve the capability of classifier and the recognition rate of noise points. Moreover, it retains the information of image edge when removing noise points, and obtains higher Peak Signal-to-Noise Ratio (PSNR).

Key words: multiple feature, weighted Support Vector Machine (SVM), Support Vector Regression (SVR), salt-pepper noise, image de-noising