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基于麻雀算法优化的OSTU分割算法
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作者:
作者单位:

1.南京信息工程大学 江苏省气象探测与信息处理重点实验室 南京 210044; 2.南京信息工程大学 江苏省大气环境与装备技术协同创新中心 南京 210044; 3 南京信息工程大学 滨江学院 无锡 214105

中图分类号:

TP391.4

基金项目:

江苏省重点研发计划社会发展项目(BE2015692)、江苏省第11批六大高峰人才项目(2014-XXRJ-006)资助


OSTU segmentation algorithm based on sparrow algorithm optimization
Author:
  • Li Peng

    Li Peng

    1.Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, China ; 2.Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China ; 3.Binjiang College,Nanjing University of Information Science and Technology, Wuxi 214105, China
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  • Ding Qianwen

    Ding Qianwen

    1.Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, China ; 2.Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
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Affiliation:

1.Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, China ; 2.Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China ; 3.Binjiang College,Nanjing University of Information Science and Technology, Wuxi 214105, China

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    摘要:

    针对传统最大类间差法（OSTU）在分割图像时计算量大、时间效率低的缺点，提出一种基于Singer混沌映射和随机游走策略的麻雀优化的OSTU分割方法（SRWSSA）。首先，利用Singer混沌映射改进初始化麻雀种群，增加初始麻雀种群的多样性，提高全局搜索能力；其次，采用随机游走策略对更新后的最优麻雀进行扰动变异，进一步增加种群多样性，增强局部搜索能力；最后，使用提出的优化算法对标准图像进行二维OSTU分割，得到最优阈值分割图像。结果表明：本文提出的SRWSSA算法在寻优能力和迭代时间上均得到了明显改善，迭代次数较PSO-OSTU、SSA-OSTU分别减少了83.3%、76%，图像峰值信噪比分别提高了8.2%、11.3%，运行时间上也有所提高，具有一定的可行性。

    Abstract:

    Aiming at the disadvantages of large amount of calculation and low time efficiency of traditional maximum inter class difference method (OSTU) in image segmentation, a sparrow optimized OSTU segmentation method (SRWSSA) based on singer chaotic map and random walk strategy is proposed. Firstly, singer chaotic map is used to improve the initialization sparrow population, increase the diversity of initialization population and improve the global search ability; Secondly, the random walk strategy is used to perturb and mutate the updated optimal sparrow, so as to further increase the population diversity and enhance the local search ability; Finally, the standard image is segmented by two-dimensional OSTU using the proposed optimization algorithm to obtain the optimal threshold segmentation image. The SRWSSA algorithm proposed in this paper has significantly improved the optimization ability and iteration time. Compared with PSO-OSTU and SSA-OSTU, the number of iterations is reduced by 83.3% and 76% respectively. The image peak signal-to-noise ratio is increased by 8.2% and 11.3% respectively, and the running time is also improved. Practice shows that this method is feasible.

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李鹏,丁倩雯.基于麻雀算法优化的OSTU分割算法[J].电子测量技术,2021,44(19):148-154

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