改进的最小交叉Tsallis熵的小目标声呐图像分割

doi:10.13229/j.cnki.jdxbgxb201403041

吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (3): 834-839.doi: 10.13229/j.cnki.jdxbgxb201403041

改进的最小交叉Tsallis熵的小目标声呐图像分割

张金果¹,郭海涛^2,3,吴君鹏³,李依桐⁴

1.东北电力大学输变电技术学院,吉林吉林 132012;
2.内蒙古大学电子信息工程学院,呼和浩特 010021;
3.东北电力大学电气工程学院,吉林吉林 132012;
4.北京邮电大学计算机学院,北京100876

收稿日期:2013-01-10 出版日期:2014-03-01 发布日期:2014-03-01
通讯作者: 郭海涛(1965),男,教授.研究方向:图像处理,信号处理.E-mail:ghtao2005@sina.com E-mail:505227895@qq.com
作者简介:张金果(1971),男,副教授.研究方向:计算机应用,控制理论与控制工程.E-mail:505227895@qq.com
基金资助:
国家自然科学基金项目(41076060);吉林省自然科学基金项目(20130101056JC);内蒙古大学高层次人才引进科研启动基金项目(135123).

Improved minimum symmetric Tsallis cross entropy for segmentation of a sonar image from a small underwater target

ZHANG Jin-guo¹,GUO Hai-tao^2,3,WU Jun-peng³,LI Yi-tong⁴

1.Power Transmission and Transformation Technology College, Northeast Dianli University, Jilin 132012, China; 2.College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China;
3.Electrical Engineering College, Northeast Dianli University, Jilin 132012, China;
4.Beijing University of Posts and Telecommunications, School of Computer Science, Beijing 100876, China

Received:2013-01-10 Online:2014-03-01 Published:2014-03-01

摘要/Abstract

摘要： 利用一维属性直方图改进交叉Tsallis熵,在此基础上提出了一种基于一维属性直方图的对称最小交叉Tsallis熵水下小目标声呐图像分割方法。该方法的主要步骤是:①抑制水下小目标声呐图像的散斑噪声;②根据图像像素的灰度值和该像素邻域的灰度平均值的大小建立属性集,在属性集上建立与该属性集约束对应的一维属性直方图;③根据一维属性直方图的对称交叉最小Tsallis熵法确定灰度二值化阈值;④对二值化后的图像去除孤立区。实验结果表明:该方法适用于直方图为复杂非双峰形状的水下小目标声呐图像,而且与现有的属性直方图上的一维最大熵阈值化法比较,具有更强的抗噪能力。

关键词: 图像处理技术, 声呐图像, 图像分割, 属性直方图, 熵, 阈值

Abstract: A segmentation method on the sonar image of a small underwater target is proposed. In this method, the gray-level threshold for segmentation is acquired via the minimum symmetric Tsallis cross entropy that is based on one-dimensional bound histogram. In this method, first, the speckle noise of the sonar image is suppressed. Second, the bound set is constructed according to the restriction in the gray-level values of both pixels and their neighborhood averages, and the one-dimensional bound histogram corresponding to that bound set is established. Third, the gray-level threshold for segmentation is determined according to the minimum symmetric Tsallis cross entropy based on the one-dimensional bound histogram. Finally, the isolated areas in the threshold image are removed. Experimental results show that the proposed method is well adequate for the images with a nonideal bimodal histogram; it has better antinoise performance than the existing methods based on the entropy of the one-dimensional bound histogram.

Key words: image processing technology, sonar image, image segmentation, bound histogram, entropy, threshold

中图分类号:

TN911.73

张金果,郭海涛,吴君鹏,李依桐. 改进的最小交叉Tsallis熵的小目标声呐图像分割[J]. 吉林大学学报(工学版), 2014, 44(3): 834-839.

ZHANG Jin-guo,GUO Hai-tao,WU Jun-peng,LI Yi-tong. Improved minimum symmetric Tsallis cross entropy for segmentation of a sonar image from a small underwater target[J]. 吉林大学学报(工学版), 2014, 44(3): 834-839.

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改进的最小交叉Tsallis熵的小目标声呐图像分割

Improved minimum symmetric Tsallis cross entropy for segmentation of a sonar image from a small underwater target

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