基于灰狼优化算法的SVM的图像噪声识别

Approach for image noise recognition by optimizing SVM using grey wolf optimization algorithm

对噪声图像进行噪声类型识别,是对噪声图像进行有针对性去噪的关键技术之一。支持向量机(SVM)是一种基于统计学习理论适用于有限样本情况的分类方法,而且它的分类能力很大程度上取决其相关参数。提出一种基于灰狼优化算法(GWO)的SVM分类方法,将GWO应用在SVM的参数寻优中,从而获得最优的分类模型;同时将该方法应用于噪声图像的噪声类型识别实验,针对高斯、椒盐、斑点这3类噪声在目标图像上形成的噪声干扰图像,分别用90个和60个干扰图像数据作为训练集和测试集,提取Zernike矩、小波高频不显著系数子带能量比这两类特征值,利用GWA-SVM分类器对干扰图像特征进行分类。实验结果表明,与传统的SVM分类器相比,GWA-SVM方法具有更好的分类准确率。

Noise type recognition for noise images is one of the key techniques for targeted denoising of noise images.Support vector machine(SVM)is a classification method based on statistical learning theory applicable to finite sample cases,and its classification ability depends largely on its related parameters.In this paper anew method is proposed to optimize the parameters of SVM.Grey wolf optimization(GWO)algorithm is used to optimize the parameter of SVM for obtain the optimal classification model,meanwhile the proposed method is applied to the noise type recognition experiment of noise images.The images withnoise interference are formed by three types of noises such as Gauss noise,Salt-and-Peppernoise and speckle noise.90sample data are taken as training samples and the remaining 60sample data are taken as the testing samples.The Zernike moments and wavelet high-frequency non-significant coefficient subband energy ratio are selected as the eigenvalue.The GWO-SVM classifier is used to classify noise images.The experimental results show that the GWO-SVM method has better classification accuracy than the traditional SVM classifier.