Signal denoising based on lifting wavelet transform and its application

Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (10): 234-237.

• 工程与应用 • Previous Articles Next Articles

Signal denoising based on lifting wavelet transform and its application

KONG Guo-jie¹,ZHANG Pei-lin^1,2,CAO Jian-jun¹,YANG Hui-yong¹

1．Ordnance Engineering College，Shijiazhuang 050003，China
2．Nanjing University of Science and Technology，Nanjing 210094，China

Received:2007-08-07 Revised:2008-01-03 Online:2008-04-01 Published:2008-04-01
Contact: KONG Guo-jie

基于提升小波变换的信号降噪及其工程应用

孔国杰¹,张培林^1,2,曹建军¹,杨惠勇¹

1．军械工程学院，石家庄 050003
2．南京理工大学，南京 210094

通讯作者: 孔国杰

Abstract

Abstract: The lifting wavelet transform is employed to construct wavelet to solve the problem of noise reduction in gun bore detection，according to the theory and characteristics of the lifting wavelet transform.A SGWT algorithm which employs interpolating subdivision is described，and then its principle of denoising is discussed.The denoising effect and the relationship between wavelet support and denoising result are investigated by experiment.The conclusion of this research work gives a reference to select wavelet for signal denoising. It is shown that better that denoising effect for gun barrel radius measuring signals in detecting system of gun bore are achieved by using the proposed method.

Key words: denoising, lifting wavelet, interpolating subdivision, gun bore detection

摘要： 为了解决炮膛检测中的噪声抑制问题，在讨论提升小波变换基本原理及其特点的基础上，采用提升小波变换的方法构造小波，介绍了基于插值细分法的提升小波，讨论了其降噪原理。通过实验对比研究了不同类型的经典提升小波和插值细分小波的降噪效果以及小波支集与降噪效果之间的关系，为选择降噪小波提供了理论依据。将该结论应用于炮膛检测系统中身管内径测量信号的降噪处理，取得了满意的效果。

关键词: 降噪, 提升小波, 插值细分法, 炮膛检测

KONG Guo-jie¹,ZHANG Pei-lin^1,2,CAO Jian-jun¹,YANG Hui-yong¹. Signal denoising based on lifting wavelet transform and its application[J]. Computer Engineering and Applications, 2008, 44(10): 234-237.

孔国杰¹,张培林^1,2,曹建军¹,杨惠勇¹. 基于提升小波变换的信号降噪及其工程应用[J]. 计算机工程与应用, 2008, 44(10): 234-237.

[1]	LIU Di, JIA Jinlu, ZHAO Yuqing, QIAN Yurong. Overview of Image Denoising Methods Based on Deep Learning [J]. Computer Engineering and Applications, 2021, 57(7): 1-13.
[2]	YANG Qian, GU Lei. Chinese Named Entity Recognition Based on Denoising Joint Character-Word Model [J]. Computer Engineering and Applications, 2021, 57(7): 151-157.
[3]	CHEN Renhe, LAI Zhenyi, QIAN Yurong. Improved Image Denoising Generative Adversarial Network Algorithm [J]. Computer Engineering and Applications, 2021, 57(5): 168-172.
[4]	AN Lei, HAN Zhonghua, LIN Shuo, SHANG Wenli. Research on GAN-SDAE-RF Model for Network Intrusion Detection [J]. Computer Engineering and Applications, 2021, 57(21): 155-164.
[5]	WANG Jie, JIN Zhengmeng, FENG Can. Adaptive Generalized Total Variation Algorithm for Poisson Noise Removal [J]. Computer Engineering and Applications, 2021, 57(20): 203-209.
[6]	HU Yaping, KONG Weiwei, LI Meng, HUANG Cuiling. Improved Panoramic Image Mosaic Algorithm for TV Image Denoising Model [J]. Computer Engineering and Applications, 2021, 57(17): 203-209.
[7]	LIU Chengshi, ZHAO Zhigang, LI Qiang, LV Huixian, DONG Xiaochen, LI Jinxia. Enhanced Low-Rank Representation Image Denoising Algorithm [J]. Computer Engineering and Applications, 2020, 56(2): 216-225.
[8]	YUAN Xiaojun, ZHOU Tao, LI Chen. Research on Image Denoising Algorithm Based on Non-local Clustering with Sparse Prior [J]. Computer Engineering and Applications, 2020, 56(18): 177-185.
[9]	CHEN Jian, LIU Ming, XIONG Peng, MENG Xianhui, YANG Lin. ECG Signal Denoising Based on Convolutional Auto-encoder Neural Network [J]. Computer Engineering and Applications, 2020, 56(16): 148-155.
[10]	YANG Yongpeng, YANG Zhenzhen, LI Jianlin, LE Jun. Low Rank and Sparse Decomposition and Its Application in Video and Image Processing [J]. Computer Engineering and Applications, 2020, 56(16): 21-30.
[11]	YAN Ding, LV Donghao, ZHANG Yong. Research on Superposition Iterative Denoising Algorithm Based on Total Variation [J]. Computer Engineering and Applications, 2020, 56(14): 226-230.
[12]	LIU Xiuping1, XUE Tingting1, HAN Lili2, DU Yongchen1, ZHANG Kaibing1, YAN Huanying3. Denoising Based on Union-of-Transforms Learning for Workpieces [J]. Computer Engineering and Applications, 2019, 55(7): 188-193.
[13]	YANG-ZHAO Qilin, PENG Dingtao, TANG Qi, LUO Xiaomin. Smoothing Quasi-Newton Algorithm for [lp] Regularization of Sparse Optimization [J]. Computer Engineering and Applications, 2019, 55(22): 163-171.
[14]	LI Min, ZHANG Guohao, ZENG Jianwei, YANG Xiaofeng, HU Xiaomin. Inception Model of Deep Convolutional Neural Network for Image Denoising [J]. Computer Engineering and Applications, 2019, 55(20): 139-144.
[15]	RAN Longcai, HUANG Chengquan, TIAN Wenying. Application Research on Improved BPDN in Image Deraining and Desnowing [J]. Computer Engineering and Applications, 2019, 55(15): 198-203.

Signal denoising based on lifting wavelet transform and its application

基于提升小波变换的信号降噪及其工程应用

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics