Research on spectral clustering met algorithm of sonar image denoising in morphological wavelet domain

LIU Guang-yu1; BIAN Hong-yu1; SHEN Zheng-yan1; SHI Hong2

doi:10.13873/j.1000-97872011.10.027

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Research on spectral clustering met algorithm of sonar image denoising in morphological wavelet domain

更新时间：2025-09-11

- Research on spectral clustering met algorithm of sonar image denoising in morphological wavelet domain
- Vol. 30, Issue 10, Pages: 41-43(2011)
- 作者机构：
  
  1. 哈尔滨工程大学水声工程学院
  2. 哈尔滨工程大学信息与通信工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872011.10.027
  CLC： TP391.41
- Published：2011
- 稿件说明：
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[1]刘光宇,卞红雨,沈郑燕,石红.形态小波域声呐图像谱聚类去噪算法研究[J].传感器与微系统,2011,30(10):41-43.

LIU Guang-yu1, BIAN Hong-yu1, SHEN Zheng-yan1, et al. Research on spectral clustering met algorithm of sonar image denoising in morphological wavelet domain[J]. 2011, 30(10): 41-43.
[1]刘光宇,卞红雨,沈郑燕,石红.形态小波域声呐图像谱聚类去噪算法研究[J].传感器与微系统,2011,30(10):41-43. DOI： 10.13873/j.1000-97872011.10.027.

LIU Guang-yu1, BIAN Hong-yu1, SHEN Zheng-yan1, et al. Research on spectral clustering met algorithm of sonar image denoising in morphological wavelet domain[J]. 2011, 30(10): 41-43. DOI： 10.13873/j.1000-97872011.10.027.

摘要

针对声呐图像噪声污染严重的问题

在基于形态小波的声呐图像去噪方法中引入了谱聚类算法以实现低信噪比下图像的去噪。给出基于形态中点小波的声呐图像去噪法

在此基础上引入谱聚类的概念

针对谱聚类能快速实现数据分类的特点

对形态中点小波分解后的高频小波系数进行分类

使得包含噪声与细节信号部分的小波系数得以分离。对分离后的两类小波系数分别进行相应的处理

通过与低频小波系数的重构即可得到去噪后的图像。仿真实验表明:该方法在信噪比等性能指标方面均优于其他小波去噪方法

验证了所提方法的可行性和有效性。

Abstract

Aiming at the serious noise pollution problems in sonar image

spectral clustering algorithm is introduced into denoising method based on morphological wavelet in order to realize image denoising with low SNR.Sonar image denoising method based on morphological midpoint wavelet is given.The concept of spectral clustering is introduced and used for the classification of high-frequency wavelet coefficients after morphological midpoint wavelet decomposition because of its fast data classification features.The wavelet coefficients including noise and detail signal can be separated.After being seperated

the two kinds of wavelet coefficients are respectively and corresponding processed.The denoised image can be obtained by the reconstruction of low-frequency wavelet coefficients.The simulation experiments show that the proposed method are better than other wavelet denoising methods and the feasibility and effectiveness of the proposed method are verified.

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references

结合非线性滤波器的形态小波域声呐图像去噪 [J]. 石红,赵春晖,沈郑燕. 哈尔滨工程大学学报 . 2010(11)

基于模糊聚类的小波变换图像去噪算法改进 [J]. 张焰林. 计算机应用与软件 . 2010(08)

基于SAR图像滤波的小波模糊聚类算法 [J]. 徐炜,贺占庄,宋军,黄士坦,杨靓. 吉林大学学报(信息科学版) . 2004(04)

基于M带小波变换与模糊聚类的图像去噪算法 [J]. 任获荣,余望好. 红外技术 . 2004(04)

一种基于形态小波的在线掌纹的线特征提取方法 [J]. 戴青云,余英林. 计算机学报 . 2003(02)

Unsupervised segmentation using a self-organizing map and a noise model estimation in sonar imagery [J] . K.C. Yao,M. Mignotte,C. Collet,P. Galerne,G. Burel. Pattern Recognition . 2000 (9)

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