基于ISSA和积分图的二维熵图像多阈值分割快速算法

吴圳桦; 唐文艳; 吕文阁; 陈汝杰; 侯梦华; 李德源

doi:10.12052/gdutxb.220148

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基于ISSA和积分图的二维熵图像多阈值分割快速算法

计算机科学与技术 | 更新时间：2026-05-27

- 基于ISSA和积分图的二维熵图像多阈值分割快速算法
- Fast Image Segmentation with Multilevel Threshold of Two-dimensional Entropy Based on ISSA and Integral Graph
- 广东工业大学学报 2023年40卷第5期页码：47-55
- 作者机构：
  
  1. 广东工业大学机电工程学院,广东,广州,510006
  2. 深圳市启灵图像科技有限公司,广东,深圳,518114
- 作者简介：
  
  吴圳桦(1997–)，男，硕士研究生，主要研究方向为机器视觉算法与应用
  吕文阁(1966–)，男，教授，博士，主要研究方向为启发式优化算法、机器视觉应用等，E-mail：lvwenge@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(51776044)
- DOI：10.12052/gdutxb.220148
  中图分类号： TP391.4
- 纸质出版：2023
- 稿件说明：
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吴圳桦, 唐文艳, 吕文阁, 陈汝杰, 侯梦华, 李德源. 基于ISSA和积分图的二维熵图像多阈值分割快速算法[J]. 广东工业大学学报, 2023, 40(5): 47-55.

Wu Zhen-hua, Tang Wen-yan, Lyu Wen-ge, et al. Fast Image Segmentation with Multilevel Threshold of Two-dimensional Entropy Based on ISSA and Integral Graph[J]. 2023, 40(5): 47-55.
吴圳桦, 唐文艳, 吕文阁, 陈汝杰, 侯梦华, 李德源. 基于ISSA和积分图的二维熵图像多阈值分割快速算法[J]. 广东工业大学学报, 2023, 40(5): 47-55. DOI： 10.12052/gdutxb.220148.

Wu Zhen-hua, Tang Wen-yan, Lyu Wen-ge, et al. Fast Image Segmentation with Multilevel Threshold of Two-dimensional Entropy Based on ISSA and Integral Graph[J]. 2023, 40(5): 47-55. DOI： 10.12052/gdutxb.220148.

摘要

为提高二维熵图像多阈值分割的性能，使其能够满足工业使用当中的实时性要求，本文提出了基于改进麻雀搜索算法(Improved Sparrow Search Algorithm

ISSA)和积分图的二维熵图像多阈值分割快速算法。首先，引入麻雀搜索算法(Sparrow Search Algorithm

SSA)并对该算法的算法性能进行分析研究，针对SSA存在的全局搜索能力差、容易陷入局部最优解的缺点，提出了基于方差线性递减的高斯扰动策略和随机步长移动策略的改进麻雀搜索算法(ISSA)。接着，进一步地引入积分图方法，降低总信息熵的运算量，并将总信息熵作为ISSA的适应度函数进行最佳阈值寻优，提出了基于ISSA并结合积分图的二维熵图像多阈值分割快速算法。最后，使用该方法与现有分割算法进行对比实验，实验结果表明，本文方法提升了图像二维熵多阈值分割的分割效率，同时在工业应用场景仍能够获得相同的效果。

Abstract

In order to improve the performance and efficiency of image segmentation with multilevel threshold of two-dimensional entropy for practical industrial applications

this paper proposes a fast image segmentation method with multilevel threshold of two-dimensional entropy based on ISSA and integral graph. Firstly

we introduce and analyze the sparrow search algorithm (SSA) . To address the shortcomings of SSA

such as poor global search ability and easy to fall into local optimal solution

we propose an improved sparrow search algorithm (ISSA) based on Gaussian perturbation strategy with linear decreasing variance and moving strategy with random step size. Then

we further introduce the integral graph method to reduce the calculation amount of the entropy

use the entropy as the fitness function of ISSA to search the optimal threshold

and propose a fast algorithm for image segmentation with multilevel threshold of two-dimensional entropy based on ISSA and integral graph. Finally

we compare the proposed method with the existing segmentation algorithms

and the experimental results show that the proposed method improves the segmentation efficiency of image segmentation with multilevel threshold of two-dimensional entropy in industrial application scenarios.

关键词

Keywords

references

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