细胞膜机制萤火虫算法优化多阈值Otsu图像分割

图像阈值分割是将灰度图像转换为二值图像的常用图像分割方式.经典多阈值Otsu算法对复杂图像进行分割取得了很好的效果,但是其采用穷举方法来寻找最优阈值是非常耗时的.针对这一问题,本文提出了一种基于细胞膜和自适应步长萤火虫混合优化算法的多阈值Otsu图像分割方法.利用萤火虫算法的启发式搜索来寻找图像分割的最优阈值很好地降低了算法的时间复杂度,并且在萤火虫算法中混合细胞膜算法很好地解决了萤火虫算法的"早熟"现象.实验结果表明,与经典多阈值Otsu法和萤火虫算法优化多阈值Otsu法相比,本文提出的算法具有更高的收敛速度和更好的图像分割效果,并且有效解决了萤火虫算法易陷入局部最优的问题.

Optimization of Multi-threshold Otsu Image Segmentation by Glowworm Swarm Algorithm with Cell Membrane Mechanism

Thresholding is a popular image segmentation method that converts gray-level image into binary image.The classical multithreshold Otsu algorithm achieved good results in segmenting complex images,but it is very time consuming to use an exhaustive approach to finding the optimum threshold value.To solve this problem,this paper proposes a multi-threshold Otsu image segmentation method based on cell membrane(CM)and adaptive step glowworm swarm optimization(A-GSO)hybrid optimization algorithm(CMA-GSO).The optimum threshold value for image segmentation is sought by heuristic search using the GSO algorithm.This method reduces the computation time of the algorithm,and solves the"premature"phenomenon of the GSO algorithm by mixing the cell membrane algorithm in the GSO algorithm.Experimental results show that compared to the classical multi-threshold Otsu method and the GSO optimized multi-threshold Otsu method,our proposed algorithm has higher convergence speed and better image segmentation effect,and effectively solves the problem that the GSO algorithm is easy to fall into the local optimal solution.