一种小波支撑向量机进行图像降噪滤波算法研究  

Research on a Wavelet Support Vector Machine for Image Denoising and Filtering Algorithm

作　　者：陈鉴清 CHEN Jianqing(College of Science,East China University of Science and Technology,Shanghai,200093 China)

机构地区：[1]华东理工大学理学院,上海200093

出　　处：《科技创新导报》2021年第22期63-65,共3页Science and Technology Innovation Herald

摘　　要：笔者基于小波分析和支撑向量机理论,提出了一种新的数字图像降噪滤波的算法,讨论了支撑向量机应用于图像降噪的可行性,并采用了一维信号支撑向量机降噪实验,论证了这种方法的可行性。本文将支持向量机方法应用于图像降噪,采用小波分析和支撑向量机核函数的理论,导出了小波核函数,并且构造出了小波支撑向量机。为了更好地获得具有高质量支持向量机,文中利用基于小波分析的理论构造小波的核函数,能较好地逼近图像与恢复理想图像;然后,结合实例,论证了支持向量机用在图像图形处理去噪方法中是否可行,并利用SAR图像拟合去噪理论,阐述了近似理论的可行性。In this paper,a new image denoising method based on wavelet analysis and Support Vector Machine regression(SVM)is presented.This paper discusses the feasibility of applying SVM to image denoising,and uses one-dimensional signal SVM denoising experiment to demonstrate the feasibility of this method.In this paper,the SVM method is applied to image denoising.Using the theory of wavelet analysis and SVM kernel function,the wavelet kernel function is derived,and the wavelet SVM is constructed.In order to obtain high-quality SVM,the kernel function of wavelet is constructed based on the theory of wavelet analysis,which can better approximate the image and restore the ideal image.Then,combined with an example,this paper demonstrates whether SVM is feasible in image processing denoising method,and expounds the feasibility of approximation theory by using SAR image fitting denoising theory.

关 键 词：图像降噪 支撑向量机 小波分析 函数逼近 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]