基于莱维飞行樽海鞘群优化算法的多阈值图像分割

Levy Flight Trajectory-based Salp Swarm Algorithm for Multilevel Thresholding Image Segmentation

针对Otsu算法用于多阈值图像分割中存在运算时间长和精度低的不足,利用群智能优化算法对图像分割算法进行优化.本文首先应用莱维飞行算法对樽海鞘群优化算法进行改进,将多阈值Otsu函数作为优化算法的适应度函数,利用改进后的LSSA寻找适应度函数的最大值,同时获得相对应的多阈值.其次,通过对几幅基本图像、伯克利大学图像分割库中的图像和实际污油图像进行多阈值Otsu分割研究,在最佳适应度值、PSNR、SSIM指标以及算法耗时方面进行对比分析.实验结果表明本文提出的算法可以获得更为准确的分割阈值和更高的分割效率.

Aiming at the shortcoming of long operation time and low precision in multi-threshold image segmentation using Otsu algorithm,image segmentation algorithm is optimized by using group intelligent optimization algorithm.In this paper,firstly,the optimized algorithm of thaliacea scabbard group is improved by using the Levy flight algorithm.The multi-threshold Otsu function is taken as the fitness function of the optimized algorithm,and the improved LSSA is used to find the maximum fitness function,and the corresponding multi-threshold value is obtained.Secondly,the multi-threshold Otsu segmentation study was conducted on several basic images,images in Berkeley university image segmentation database and actual images of pollution oil,and the comparative analysis was conducted on the optimal fitness value,PSNR,SSIM index and algorithm time.Experimental results show that the proposed algorithm can obtain more accurate segmentation thresholds and higher segmentation efficiency.