摘要
针对传统边缘检测算法边缘提取数量较少和抑制噪声能力较弱等问题,提出一种融合Canny自适应算法和小波模极大值法的边缘检测方法。首先将源图像分别采用Canny自适应算法和小波模极大值法进行边缘提取,得到高、低频边缘图像,再将上述两边缘图像进行加权融合并优化处理。多组图像实验结果表明融合算法优于单独使用Canny自适应算法或小波模极大值法,为提取连续、完整的边缘特征提供了一种可行思路。
Aiming at the problem that the traditional edge detection algorithm has few edge extraction and is unable to control noisy well, the paper proposes an edge detection method based on Canny adaptive algorithm and wavelet transform modulus maxima algorithm. Firstly, source image is extracted with Canny adaptive algorithm and wavelet transform modulus maxima algorithm respectively to get the edge images of high and low frequency. Then the edge images gained by the above two methods are fused by weight and optimized. Many image datasets of experimental results show that the fusion algorithm is superior to Canny adaptive algorithm or wavelet modulus maxima algorithm alone, and provides a feasible method for extracting continuous and complete edge features.
作者
赵静
杨化超
ZHAO Jing YANG Hua- chao(School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou Jiangsu 221116, China)
出处
《计算机仿真》
北大核心
2017年第6期277-280,共4页
Computer Simulation
关键词
小波变换模极大值
边缘提取
图像融合
Wavelet transform modulus maxima
Edge detection
Image fusion
