结合小波滤波和形态学的图像边缘检测方法  

Method of image edge detection based on wavelet filter and morphology

作　　者：黄剑玲[1] 邹辉[2] 

机构地区：[1]上饶师范学院数学与计算机科学学院,江西上饶334000 [2]上饶职业技术学院机械系,江西上饶334000

出　　处：《计算机工程与应用》2012年第19期187-190,242,共5页Computer Engineering and Applications

基　　金：江西省教育厅科学技术研究项目(No.GJJ11608)

摘　　要：针对传统的边缘检测方法对含噪图像检测效果不理想,提出了一种小波滤波和多结构元素的数学形态学相结合的图像边缘检测方法。用广义交叉验证准则进行小波阈值的自适应选取,用此阈值的广义阈值函数的小波滤波方法对含噪图像去噪;构造4种具有代表性的结构元素,根据边缘方向自动选择相应方向的结构元素,用改进的形态学边缘检测算子对图像进行边缘检测,得到在噪声存在条件下较为理想的图像边缘。实验结果表明,该算法能够有效地抑制噪声,检测的边缘较清晰、连续,其检测效果优于传统边缘检测算法。While the traditional methods for image edge detection of images containing noises are insufficient for today＇ s use, this paper presents a new and better method for image edge detection which is based on wavelet filter and mathematical morphology. This method uses Generalized Cross Validation （GCV） standard to select the wavelet threshold adaptively and cleans noises contained in images by wavelet filter based on generalized threshold function of the selected threshold. The method constructs 4 representative structure elements, selects automatically the appro- priate structure elements according to the direction of the edge, and uses an improved morphological edge-detecting operator to detect image edges. And it is proved that this method can give relatively perfect results of image edges＇ detection in the condition of noise.The results of this experiment show that the method can effectively suppress noise and make the detected edges more consistent and clearer. Above all, the effect of the detection is much better than that of those traditional ones.

关 键 词：小波滤波 数学形态学 边缘检测 广义交叉验证 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]