分区均值的自适应噪声图像边缘检测算法  

Subzone mean adaptive edge detection algorithm for noisy images

作　　者：沈德海[1] 侯建[1] 鄂旭[1] 张龙昌[1] 

机构地区：[1]渤海大学信息科学与技术学院,辽宁锦州121013

出　　处：《现代电子技术》2015年第8期1-3,7,共4页Modern Electronics Technique

基　　金：国家自然科学基金资助项目:基于博弈论的高效稳定聚类算法研究(61473045);博士后基金项目(2012M520158);辽宁省"百千万人才工程"资助项目(2012921058);辽宁省高等学校实验室项目(L2012397);教育厅科研一般项目(L2012400)

摘　　要：针对边缘检测算法存在的检测精度与抑噪的矛盾,提出一种基于新的图像边缘检测算法。算法将检测窗口按照0o,45o,90o和135o四个不同方向分别划分为两个子区域,先统计每个检测窗口(3×3)内脉冲噪声点的个数,如果超过3个,则扩大检测窗口至5×5。对于检测窗口每个方向划分的两个子区域,分别计算区域内的非噪声点的平均灰度值,利用平均值差的绝对值作为窗口的方向梯度值,进而求得中心点的梯度。然后,对梯度图像采用改进的非极大值抑制方法进行细化,并提取边缘。实验结果表明,该算法检测的图像边缘方向性较强,边缘较细,不仅对不同程度脉冲噪声干扰图像具有较强的抑噪能力,而且对高斯噪声也具有一定程度的抑制效果,算法具有较强的适应性。In view of contradiction between detection accuracy and noise suppression of edge detection algorithm,a new edge detection algorithm is proposed. In the algorithm,the detection window is divided into two subzones in each direction of 0°,45°,90° and 135°. The number of pulse noise points in detection window of 3 × 3 is counted. If the point number is more than 3,the detection window size is extended to 5 × 5. For two subzones in each direction of detection window,the mean gray value of nonnoise points in every subzone is calculated respectively. The absolute value of the two mean values′ difference is taken as direction gradient value to acquire the gradient of center point,and then the improved nonmaximum suppression method is used to refine gradient image and extract the image edge. The experiment results show that directionality of the image edge detected by the new algorithm is well and the edge is fine,and the algorithm can suppress different degree impulse noise and has a certain suppression effect on Gaussian noise and strong adaptability.

关 键 词：边缘检测 噪声图像 分区 自适应 

分 类 号：TN911.73[电子电信—通信与信息系统]