| WU Jin (吴 进),XIONG Hao,LUO Wenxuan,GUO Linlin.[J].高技术通讯(英文),2025,31(4):365~372 |
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| Multi-strategy improved red-billed blue magpie optimizer for Kapur multi-threshold image segmentation |
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| DOI：10. 3772 / j. issn. 1006-6748. 2025. 04. 006 |
| 中文关键词: |
| 英文关键词: red-billed blue magpie optimizer, image segmentation, multi-threshold, Kapur maximum entropy |
| 基金项目: |
| Author Name | Affiliation | | WU Jin (吴 进) | (School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121,P. R. China) | | XIONG Hao | | | LUO Wenxuan | | | GUO Linlin | |
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| 中文摘要: |
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| 英文摘要: |
| Multi-threshold image segmentation techniques based on intelligent optimization algorithms show great potential in low-cost, real-time applications. These methods are efficient even with limited computational resources. This paper proposes a multi-strategy improved red-billed blue magpie optimizer (MIRBMO) for Kapur multi-threshold image segmentation, aiming to enhance segmentation quality. First, Sobol sequences with elite reverse learning are used to optimize the distribution of the initial population, accelerating the optimization process. Second, lens imaging reverse learning is introduced to help the algorithm escape local optima. Finally, the golden sine strategy is adopted to increase the search space diversity and explore potential optimal solutions. The algorithm’s performance is evaluated using the 8 classic benchmark test functions, and results show that MIRBMO outperforms red-billed blue magpie optimizer ( RBMO) in optimization capability and demonstrates clear advantages over other intelligent optimization algorithms. When applied to Kapur multi-threshold segmentation, MIRBMO yields a threshold combination with higher entropy values and produces segmented images with superior peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity index measure (FSIM) values, indicating its strong application potential. |
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