文章摘要
夏 平、,刘小妹、,吴涛、,雷帮军、.基于Contourlet域HMT模型的声纳图像去噪[J].,2016,35(1):50-57
基于Contourlet域HMT模型的声纳图像去噪
Sonar Image De-noising Based on HMT Model in Contourlet Domain
投稿时间:2015-05-13  修订日期:2015-12-28
中文摘要:
      声纳图像预处理是声纳图像目标识别与跟踪的前提;受成像环境影响,声纳图像对比度低,特性信息较弱,且分辨率不高,传统的光学图像去噪方法应用于声纳图像效果较差,为此,提出了Contourlet域HMT模型(CT-HMT)的声纳图像去噪算法。Contourlet分析除具有小波的多分辨率分析特性外,还具有良好的方向选择性,能有效地提取声纳图像的弱特征信息,为HMT中父、子节点参数的描述能准确地反映树结构的分布规律和图像统计特性奠定了基础;Contourlet域中,不同方向间子带系数的相关性体现于DFB分解中,相邻尺度间父节点对应的4个子节点分布在2个可分离的方向子带上,父、子节点状态“持续性”具有一阶Markov性;尺度内Contourlet系数的“聚集性”采用混合高斯模型建模;最后,依据贝叶斯准则估计无噪图像的Contourlet系数,实现声纳图像去噪。从视觉效果和定量分析两方面对实验结果验证表明,本文算法能有效地抑制噪声,提取声纳图像的弱特征信息,较好地保留了图像的边缘和轮廓信息。
英文摘要:
      Sonar image preprocessing is the precondition of object recognition and tracking. For the impact of imagery environmental factor, the sonar image has the disadvantages of low contrast ratio, weak feature information, the coarse resolution and so on. Compared with common optical image, it is difficult to get well de-noising results by using traditional de-noising algorithm. Therefore, a sonar image de-noising algorithm based on Contourlet domain HMT (CT-HMT) model is proposed. Contourlet analysis not only has inherited the multiresolution analysis characteristic of wavelet, but also has better directional selectivity, which can effectively extract the weak characteristic information of sonar image. It is a foundation for the parameters of the parent-child nodes to accurately reflect the distribution of tree structure and statistical property of sonar image in the HMT model. In Contourlet domain, the correlation of the sub-band coefficients between different directions is embodied in the DFB decomposition. Between adjacent scales, the four corresponding child nodes of the parent node are distributed on two separable sub-bands, and the status of parent-child has persistent property of first-order Markov. The aggregation of the Contourlet coefficients on the intra-scale is modeled by mixture gauss model. Finally, estimate the Contourlet coefficients of the no-noise image depending on Bayesian principle to realize the sonar image de-noising. From two aspects of visual effects and quantitative analysis, the experimental results show that the algorithm can effectively suppress noise and extract weak information of sonar image, and can better keep the edge and contour information of the image.
DOI:10.11684/j.issn.1000-310X.2016.01.008
中文关键词: 声纳图像去噪  Contourlet分析  隐马尔科夫树模型(HMT)  方向滤波
英文关键词: Sonar image de-noising  Contourlet analysis  Hidden Markov tree model  Directional Filter
基金项目:国家自然科学基金(联合基金)重点项目(U1401252);国家自然科学(61272237);楚天学者科研基金项目(KJ2012B001).
作者单位E-mail
夏 平、 三峡大学 水电工程智能视觉监测湖北省重点实验室
三峡大学 计算机与信息学院 
13972603620 
刘小妹、 三峡大学 水电工程智能视觉监测湖北省重点实验室
三峡大学 计算机与信息学院 
1074309824@qq.com 
吴涛、 三峡大学 水电工程智能视觉监测湖北省重点实验室
三峡大学 计算机与信息学院 
865668341@qq.com 
雷帮军、 三峡大学 水电工程智能视觉监测湖北省重点实验室
三峡大学 计算机与信息学院 
1390703228@qq.com 
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