肖静,游世辉.基于小波变换的发动机表面缺陷图像去噪方法的研究[J].表面技术,2018,47(12):328-333.
XIAO Jing,YOU Shi-hui.Denoising Method of Engine Surface Defect Image Based on Wavelet Transform[J].Surface Technology,2018,47(12):328-333
基于小波变换的发动机表面缺陷图像去噪方法的研究
Denoising Method of Engine Surface Defect Image Based on Wavelet Transform
投稿时间:2018-08-01  修订日期:2018-12-20
DOI:10.16490/j.cnki.issn.1001-3660.2018.12.044
中文关键词:  发动机表面缺陷图像  图像去噪  小波变换  支持向量机  插值运算
英文关键词:engine surface defect image  image denoising  wavelet transform  support vector machine  interpolation operation
基金项目:江西省科技厅工业支撑项目(20111BBE50011)
作者单位
肖静 1.九江学院 机械与材料工程学院,江西 九江 332005 
游世辉 2.湘潭大学 土木工程与力学学院,湖南 湘潭 411105 
AuthorInstitution
XIAO Jing 1.School of Mechanical & Material Engineering, Jiujiang University, Jiujiang 332005, China 
YOU Shi-hui 2.School of Civil Engineering and Mechanics, Xiangtan University, Xiangtan 411105, China 
摘要点击次数:
全文下载次数:
中文摘要:
      目的 滤除发动机表面缺陷图像上的噪声,使发动机表面缺陷信息得以更好地呈现。方法 首先利用小波变换将发动机表面缺陷含噪图像进行系数分解,获取不同的小波系数;接着利用支持向量机对小波分解系数进行分类,以达到将噪声信号与非噪声信号进行分离的效果;最后利用插值运算对硬阀值函数进行优化,以克服函数不连续性引起的振铃效应等弊端,使得去噪后图像能够保持更多的细节信息。通过实验仿真将所提方法以及中值滤波、双边滤波方法的去噪效果进行对比。结果 所提方法去噪后图像与中值滤波以及双边滤波方法去噪后图像相比,具有更高的PSNR值以及SSIM值。测试图像噪声强度为25%时,所提方法去噪后图像的PSNR值以及SSIM值较中值滤波方法去噪分别提高了20.66%以及11.89%,较双边滤波方法去噪分别提高了10.30%以及5.48%。结论 所提方法比中值滤波、双边滤波方法具有更好的去噪效果,能够对发动机表面缺陷图像的噪声进行去除,并较好地保留图像的细节信息。
英文摘要:
      The work aims to filter the noise from the engine surface defect image so that the engine surface defect information can be better presented. Firstly, wavelet transform was used to decompose the noise image of engine surface defect to obtain different wavelet coefficients. Then, SVM was used to classify the wavelet decomposition coefficients in order to separate the noise signal from the non-noise signal. Finally, interpolation was used to optimize the hard threshold function to overcome the ringing effect caused by the discontinuity of the function, so that the denoised image could keep more details. The denoising effect of the proposed method was compared with that of the median filter and the bilateral filter through experimental simulation. The image denoised by the proposed method had higher PSNR and SSIM values than that denoised by median filtering and bilateral filtering methods. When the noise of tested image was 25%, the PSNR value and SSIM value of the image denoised by the proposed method increased by 20.66% and 11.89% respectively, compared with that denoised by the median filtering method and increased by 10.30% and 5.48% respectively, compared with that denoised by bilateral filter. Therefore, the proposed method has better denoising effect than the median filter and bilateral filter. It can remove the noise of the engine surface defect image and retain the details of the image better.
查看全文  查看/发表评论  下载PDF阅读器
关闭

关于我们 | 联系我们 | 投诉建议 | 隐私保护 | 用户协议

您是第19515443位访问者    渝ICP备15012534号-3

版权所有:《表面技术》编辑部 2014 surface-techj.com, All Rights Reserved

邮编:400039 电话:023-68792193传真:023-68792396 Email: bmjs@surface-techj.com

渝公网安备 50010702501715号