基于多尺度小波和Tsallis熵的水下图像边缘检测  

Underwater image edge detection based on multi-scale wavelet and Tsallis entropy

作　　者：王晓琦 赵宣植 刘增力[1,2] WANG Xiao-qi;ZHAO Xuan-zhi;LIU Zeng-li(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500;Yunnan Key Laboratory of Artificial Intelligence,Kunming University of Science and Technology,Kunming 650500,China)

机构地区：[1]昆明理工大学信息工程与自动化学院,云南昆明650500 [2]昆明理工大学云南省人工智能重点实验室,云南昆明650500

出　　处：《计算机工程与科学》2023年第7期1245-1252,共8页Computer Engineering & Science

基　　金：国家自然科学基金(61271007)。

摘　　要：针对水下图像对比度低和边缘模糊的问题,提出一种基于多尺度小波和Tsallis熵的水下图像边缘检测算法。首先,结合多尺度小波分解特性,采用开放暗通道模型移除低频雾霾现象和软阈值操作降低高频噪声;其次,采用二维高斯函数构造高斯尺度空间进行背景估计,以区分背景与目标信息;最后,结合信息熵和Tsallis熵求得最优阈值,从而得到边缘检测图像。实验结果表明,该算法能有效检测出退化水下图像的边缘轮廓信息,去除虚假边缘情况,准确提取图像的特征边缘。同时应用测试显示,该算法在大气雾霾图像的边缘检测方面表现出色。To address the problem of low contrast and edge blurring in underwater images,a multi-scale wavelet and Tsallis entropy-based underwater image edge detection algorithm is proposed.Firstly,combining the characteristics of multi-scale wavelet decomposition,the open dark channel model is used to remove low-frequency haze and the soft threshold operation is used to reduce high-frequency noise.Secondly,a two-dimensional Gaussian function is used to construct a Gaussian scale space for background estimation to distinguish background from target information.Finally,the optimal threshold is obtained by combining information entropy and Tsallis entropy,and the edge detection image is obtained.Experimental results show that the proposed algorithm can effectively detect the edge contours of degraded underwater images,remove false edge situations,and accurately extract the feature edges of the image.At the same time,tests show that the algorithm performs well in edge detection of atmospheric haze images.

关 键 词：水下图像 边缘检测 多尺度小波 TSALLIS熵 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]