Improving Wavelet Threshold De-noising Applied on Parts Detection

摘要：

　　The traditional filtering methods such as median filter and mean filter always blurrs image features,resulting in poor noise reduction effect.Wavelet transform has unique adaptability due to its variable resolution,which can better implement wavelet denoising on the basis of image feature.Aiming at the shortcoming of traditional wavelet transform threshold denoising,based on the hard threshold and soft threshold function,this paper proposes improved adaptive thresholding function.By comparing and validating,this method obtains the smaller mean square error(MSE)and higher peak signal to noise ratio.Meanwhile,this method improves the quality of detection images,and reduces the impact on images brought by noise from external enviroment and internal system.So,this can be applied to image noise reduction of the detection system.

作者: Cheng Lizhou Mao Jian Wei Hongyuan

作者单位: School of Mechanical and Automotive Engineering,Shanghai University of Engineering Science,Shanghai,201600,China

会议类型: 国际会议

会议名称: 2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)(2018第二届电子信息技术与计算机工程国际会议)(EITCE2018)

会议地点: 上海

会议语种:英文

页码: 1-5

在线出版日期: 2018-10-12（万方平台首次上网日期，不代表论文的发表时间）