系统工程与电子技术 ›› 2021, Vol. 43 ›› Issue (2): 318-327.doi: 10.12305/j.issn.1001-506X.2021.02.05

• 电子技术 • 上一篇    下一篇

基于改进麻雀搜索算法的多阈值图像分割

吕鑫1,2(), 慕晓冬1(), 张钧2()   

  1. 1. 火箭军工程大学作战保障学院, 陕西 西安 710025
    2. 北京遥感设备研究所, 北京 100854
  • 收稿日期:2020-08-14 出版日期:2021-02-01 发布日期:2021-03-16
  • 作者简介:吕鑫(1996-),男,硕士研究生,主要研究方向为群智能算法优化及其在图像分割中的应用。E-mail:2822795340@qq.com|慕晓冬 (1965-),男,教授,博士研究生导师,博士,主要研究方向为智能信息处理。E-mail:wascom4@sina.com|张钧 (1973-),男,研究员,博士,主要研究方向为先进导引头技术。E-mail:zhang_jun25@sina.com

Multi-threshold image segmentation based on improved sparrow search algorithm

Xin LYU1,2(), Xiaodong MU1(), Jun ZHANG2()   

  1. 1. Operational Support Academy, Rocket Force University of Engineering, Xi'an 710025, China
    2. Beijing Institute of Remote Sensing Equipment, Beijing 100854, China
  • Received:2020-08-14 Online:2021-02-01 Published:2021-03-16

摘要:

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。

关键词: 图像分割, 改进麻雀搜索算法, 多阈值, 最大类间方差, Kapur熵

Abstract:

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.

Key words: image segmentation, improved sparrow search algorithm (ISSA), multi-threshold, Otsu, Kapur entropy

中图分类号: