萤火虫算法的图像边缘检测

Image Edge Detectionof Glowworm Swarm Algorithm

针对传统边缘检测算法自适应能力差、固定阈值和背景噪声抑制差的问题,提出了一种基于萤火虫算法的图像边缘测算法。该算法首先通过灰度图像矩阵的一阶导数得到灰度图像的梯度值矩阵,然后使用萤火虫算法,即根据萤火虫的发光和吸引特性,根据其搜索区域范围寻找其它萤火虫,并向邻域内位置内亮度高的萤火虫位置移动,从而实现搜索图像的梯度最大值,检测出图像的边缘。仿真实验结果表明,该算法能快速、准确地检测出图像的边缘,因此将其应用于图像的边缘检测是可行、有效的。

Aiming at the deficiencies of low self-adaptive abilities, fixed threshold and weak background noise suppression, an image edge detection algorithm based on GSO （Glowworm Swarm） is proposed for the first time. This algorithm first acquires the gradient matrix of grayscale by first-order derivative of grayscale matrix, then, glowworm swarm algorithm is used to find other glowworm according to/lashing and attracting characteristics of the glowworm and the region of search, thus moving towards the luminant glowworm so as to achieve the maximum of the searched image gradient and detect the image edge. Simulation experiment indicates that this algorithm could detect the image edge rapidly and accurately, and thus is feasible and effective for its application in image edge detection.