基于余弦高斯核函数的非局部均值煤尘图像去噪  

Non-localMean Coal Dust Image Denoising based on Cosine Gaussian Kernel Function

作　　者：李楠[1] 梁超[2] LI Nan;LIANG Chao(College of Information and Control Engineering,Jilin Institute of Chemical Technology,Jilin City 132022,China;College of Computer Science and Engineering,Changchun University of Technology,Changchun City 130012,China)

机构地区：[1]吉林化工学院信息与控制工程学院,吉林吉林132022 [2]长春工业大学计算机科学与工程学院,吉林长春130012

出　　处：《吉林化工学院学报》2019年第3期38-41,共4页Journal of Jilin Institute of Chemical Technology

基　　金：吉林省科技攻关计划重点科技攻关项目(20150204020SF)

摘　　要：煤尘是引发煤矿事故的主要诱因,煤尘颗粒的分类测量对煤尘浓度的在线检测至关重要.近几年,颗粒图像分析处理技术的应用越来越广泛,但是煤矿井下环境复杂,煤尘图像在采集和传输的过程中,不可避免的会受到噪声的干扰,对后续的颗粒检测产生影响.因此,煤尘颗粒图像的去噪处理就显得十分重要.非局部均值去噪算法(Non-Local Means,NLM)在图像去噪方面效果显著,但是对于经典NLM,使用指数函数作为核函数会造成图像细节的缺失.为了改进这一缺陷,本文采用余弦加权的高斯核函数对传统的非局部均值算法进行改进,能够更好的保留去噪后图像的细节.通过实验结果表明,该算法的去噪性能明显优于经典NLM算法,能更好地保留煤尘图像中的细节信息.Coal dust is the main cause of coal mine accidents.The classification and measurement of coal dust particles is very important for online detection of coal dust concentration.In recent years,particle image analysis and processing technology has been applied more and more widely,but the underground environment of coal mine is complex,coal dust image in the process of collection and transmission,will inevitably be affected by noise interference,on the subsequent particle detection.Therefore,it is very important to de-noising the image of coal dust particles.Non-local Means(NLM)denoising algorithm has a significant effect on image denoising,but for classical NLM,the use of exponential function as the kernel function will result in the loss of image details.In order to improve this defect,this paper USES cosine weighted gaussian kernel function to improve the traditional non-local mean algorithm,which can better retain the details of the denoised image.Experimental results show that the denoising performance of this algorithm is significantly better than the classical NLM algorithm,and it can better retain the detailed information in the coal dust image.

关 键 词：非局部均值 加权平均 图像去噪 煤尘图像 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]