噪声图像中提取边缘的蚁群搜索算法

该文提出一种边缘引导的蚁群搜索算法,以解决常用的边缘提取方法抑制噪声能力不强,提取边缘不连续的缺点.此算法首先进行边缘检测获取由真实边缘和噪声组成的可能边缘点;然后利用可能边缘信息引导蚁群迭代搜索局部边缘曲线,并根据蚂蚁搜索曲线的长度更新其行走路径上的信息素分布,使搜索逐渐向真实的边缘收敛;最后,依据信息素遗留提取真实的边缘曲线.相对传统的蚁群算法,该文利用边缘信息引导蚁群搜索,增强了搜索的目的性,提高了算法效率.多组噪声图像的实验表明:该算法能够有效地从噪声图像中提取物体的真实边缘,在最大限度地保留细节信息的同时抑制噪声.

Ant Colony Search for Edge Extraction in Noise Image

Traditional edge extracting methods are sensitive to image noise, and discontinuities often occur in extracted edges. This paper presents an edge leading ant colony algorithm to suppress the noise for edge extraction in noise image. Firstly, it detects the possible edge points which include the real edge points and the noise points. Then, the information of possible edge points is used as heuristic measure to guide iteratively searches of ants to get local edge points. In each cycle, pheromones on the traversed route of each ant are updated proportional to the length of the route, and the searching routes converge on real edges progressively based on the pheromone updating rule. Finally, real edges can be extracted according to the intensity of pheromones. Compared with traditional ant colony algorithms, the proposed method uses leading information to guide the searching process of the ants, which enhances the intention of the search, and improves the efficiency of the algorithm. Experimental results on noise images show that the method can extract real edges effectively, which keeps the edge details and suppresses the noise at the same time.