结合图像增强的含噪齿轮图像边缘检测研究  

Edge detection of noisy gear image combined with image enhancement

作　　者：肖文韬 李登峰 XIAO Wen-tao;LI Deng-feng(School of Mathematics and Computer,Wuhan Textile University,Wuhan 430200,China)

机构地区：[1]武汉纺织大学数学与计算机学院,湖北武汉430200

出　　处：《机电工程》2021年第7期918-922,共5页Journal of Mechanical & Electrical Engineering

基　　金：国家自然科学基金资助项目(61471410)。

摘　　要：为了提高齿轮后期故障检测和缺陷检测的效果,并有效去除图像中的混合噪声,提出了一种结合图像增强的含噪齿轮图像边缘检测算法。首先,引入了信息熵改进了马氏距离公式,并将马氏距离用于改进自适应中值滤波器;然后,对幂次变换进行了改进,使其具有自适应性,并将改进的幂次变换用于改进Retinex算法,对图像整体进行了增强;最后,采用小波模极大值法对含噪齿轮图像进行了边缘检测实验。研究结果表明:在对混合噪声的客观评价上,该算法的客观评价指标PSNR、SNR和SSIM均比中值滤波和自适应中值滤波指标高,并且其去噪效果也有明显提升;同时,利用改进Retinex算法对图像进行增强后,图像整体亮度和对比度有所增加,对部分噪声有所抑制,使得含噪齿轮图像边缘检测效果更好。In order to improve the effect of gear fault detection and defect detection,and effectively remove the mixed noise in the image,an edge detection algorithm of noisy gear image combined with image enhancement was proposed.First,the information entropy was introduced to improve the Mahala Nobis distance formula,and the Mahala Nobis distance was used to improve the adaptive median filter.Then the power transform was improved to make it adaptive,and the improved power transform was used to improve the Retinex algorithm to enhance the overall image.Finally,the wavelet modulus maximum method was used to detect the edge of the noisy gear image.The research results show that the objective evaluation indexes PSNR,SNR and SSIM of this algorithm are higher than the objective evaluation indexes of median filter and adaptive median filter for mixed noise,and the denoising effect is significantly improved.At the same time,combining with the improved Retinex algorithm,the enhanced brightness and contrast of the overall noisy gear image are increased,and part of the noise is suppressed,making the edge detection effect of noisy gear image better.

关 键 词：混合噪声 马氏距离 RETINEX算法 小波模极大值 

分 类 号：TH132.41[机械工程—机械制造及自动化] TP391.4[自动化与计算机技术—计算机应用技术]