基于改进麻雀搜索算法的多阈值图像分割

针对传统多阈值图像分割方法中存在的分割精度低、计算量大、分割速度慢等问题,提出了一种基于改进麻雀搜索算法(improved sparrow search algorithm, ISSA)的多阈值图像分割方法。首先,结合鸟群算法(bird swarm algorithm, BSA)中飞行行为的思想优化麻雀搜索算法(sparrow search algorithm, SSA),并采用4种类型的基准函数评估ISSA的寻优性能。然后,进行基于类间方差和Kapur熵的多阈值图像分割,并对比两种方法的分割结果。最后,采用PSNR、目标函数值和标准差作为评估标准,将ISSA与现有分割算法进行对比分析。结果表明, ISSA具有更优的搜索能力和开拓能力,且分割速度和分割精度均得到提升。

Multi-threshold image segmentation based on improved sparrow search algorithm

To solve the problems of low segmentation accuracy,large calculation amount and slow segmentation speed in the traditional multi-threshold image segmentation methods,a multi-threshold image segmentation method based on improved sparrow search algorithm(ISSA)is proposed.First,the sparrow search algorithm(SSA)is optimized based on the flight behavior in the bird swarm algorithm(BSA),and four types of benchmark functions are used to evaluate the optimization performance of ISSA.Then,the between-class variance and Kapur entropy are used to perform the multi-threshold image segmentation,and the segmentation results of the two methods are compared.Finally,using PSNR,objective function value and standard deviation as evaluation criteria,ISSA is compared with the existing segmentation algorithms.The results show that ISSA has better search ability and development ability,and it has a significant improvement in terms of segmentation speed and accuracy.