基于双密度双树复数小波和模糊优化的声呐图像目标增强方法研究  

TARGET ENHANCEMENT METHOD OF SONAR IMAGE BASED ON DUAL DENSITY DUAL TREE COMPLEX WAVELET AND FUZZY OPTIMIZATION

作　　者：张静 胡健阳 段先华 Zhang Jing;Hu Jianyang;Duan Xianhua(School of Computer Science,Jiangsu University of Science and Technology,Zhenjiang 212000,Jiangsu,China)

机构地区：[1]江苏科技大学计算机学院,江苏镇江212000

出　　处：《计算机应用与软件》2025年第4期245-250,共6页Computer Applications and Software

基　　金：国家自然科学基金项目(61806087);江苏省研究生创新项目(SJCX20_1475)。

摘　　要：为了更好地消除噪声对声呐图像的影响,提高声呐图像目标的识别质量,提出一种双密度双树复数小波变换(DDDTCWT)和模糊优化的方法。该方法通过双树结构消除因间隔采样丢失实用信息的缺陷,对分解后的低频分量采用改进的模糊优化算法,对高频分量进行双变量收缩函数处理,经过逆小波变换,得到增强后的图像。经实验证明,该算法能够较好地保留图像的细节信息,使图像的层次更加分明,并有很好的视觉效果。该算法的主观效果和客观指标明显优于其他算法。In order to eliminate the influence of noise on sonar image and improve the recognition quality of sonar image,a method of dual-density dual-tree complex wavelet transform(DDDTCWT)and fuzzy optimization is proposed.The method eliminated the defect of losing practical information due to interval sampling by dual-tree structure,adopted the improved fuzzy optimization algorithm for the decomposed low-frequency components,and performed the dual-variable contraction function processing for the high-frequency components.The enhanced image was obtained through inverse wavelet transform.Experiments show that the proposed algorithm can better retain the details of the image,can make the image level more clear,and has a very good visual effect.The subjective effect and objective index of this algorithm are obviously better than other algorithms.

关 键 词：双密度双树复数小波 模糊优化理论 声呐图像 目标增强 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]