联合NSCT模糊域的声呐图像目标增强方法研究  

Research on sonar image target enhancement method combined with NSCT fuzzy domain

作　　者：马启星 曾庆军[1] 胡健阳 MA Qi-xing;ZENG Qin-jun;HU Jian-yang(School of Electronic Information,Jiangsu University of Science and Technology,Zhenjiang 212000,China;School of Computer Science,Jiangsu University of Science and Technology,Zhenjiang 212000,China)

机构地区：[1]江苏科技大学电子信息学院,江苏镇江212000 [2]江苏科技大学计算机学院,江苏镇江212000

出　　处：《舰船科学技术》2022年第7期138-141,共4页Ship Science and Technology

基　　金：国防基础科研计划项目(JCKY2017414C002);国家自然科学基金资助项目(11574120);江苏省产业前瞻与共性关键技术(BE2018103)。

摘　　要：提出一种基于非下采样轮廓波变换(NSCT)结合模糊域的噪声抑制和目标增强方法。使用NSCT将含噪声呐图像分解为不同的频率和方向响应子带,利用V-Bayes估计理论推导了模型的相应非线性二元阈值函数对NSCT系数的子带进行去噪。结合领域信息和空间信息构造模糊特征,再构建出模糊隶属度收缩函数对降噪后的NSCT分量执行二次降噪,最后以重建降噪后的声呐图像数据,使用综合数据和效果图示例来证明所提出的方法能够有效的去除声呐图像噪声,且优于基于小波变换的阈值去噪方法。A non-subsampled contourlet transform(NSCT) combined with adaptive threshold is proposed for noise suppression and target enhancement. First, NSCT can be used to decompose noise-containing sonar images into different frequency and directional response subbands. Then, we use the adaptive threshold operator to de-noise the subband of the NSCT coefficient. Combined with the feature of field information and spatial information to construct the fuzzy, to build up the fuzzy membership degree after the contraction function of noise reduction of NSCT secondary noise component implementation, and finally to rebuild the sonar image data of the noise reduction after using sample data to prove that the proposed method can effectively remove noise sonar image, and is superior to the threshold denoising method based on wavelet transform.

关 键 词：声呐图像 NSCT 模糊域 平移不变性 变分贝叶斯 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]