关键词: ε-支持向量回归机, ε不敏感损失函数, 图像去噪

Abstract: Aiming at traditional methods of image denoising are unable to acquire define edge information of image.Then a new approach of image denoising based on support vector regression（SVR） is put forward. ε-SVR can achieve robust regression by introducing ε insensitive loss function.Regression is sparse and holds all the merits of SVM.In this paper theory of ε-SVM and its application in image denoising is analyzed.We compare image denoising effects between SVM and traditional image filtering.And we compare image denoising effects of SVM kernel function under the condition of different noises.Effects of order parameter of multinomial kernel on image filtering are also discussed.Experimental results demonstrate thatε-SVM works well for image denosing and has good performance of sparsity.

Key words: ε-SVR, ε insensitive loss function, image denoising