| 基于自适应细菌觅食算法的灰度图像增强方法 |
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| 引用本文： | 翟自勇,赵卫国,王欢.基于自适应细菌觅食算法的灰度图像增强方法[J].河北工程大学学报,2013,30(1):77-81. |
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| 作者姓名： | 翟自勇 赵卫国 王欢 |
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| 作者单位： | 河北工程大学技术中心,河北邯郸,056038 |
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| 基金项目： | 河北省高等学校科学研究计划项目（项目编号：2011138） |
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| 摘 要： | 为提高图像增强的自适应性,首先将细菌的自适应趋向因子引入到细菌觅食算法中,然后将提升的细菌觅食算法和非完全Beta函数结合而去获得最佳的灰度变换参数,最终实现对降质图像的最大程度的自适应增强.仿真实验结果表明,提升的优化算法可以更好的优化Beta函数的参数,因而能更有效地提高图像整体对比度和视觉效果.
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| 关 键 词： | 细菌觅食算法 图像增强 优化算法 趋向因子 |
| 收稿时间： | 2012/10/22 0:00:00 |
Adaptive bacterial foraging optimization for grey image enhancement |
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| Authors: | ZHAI Zi-yong ZHAO Wei-guo and WANG Huan |
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| Affiliation: | The Modern Education Technology Center, Hebei University of Engineering, Hebei Handan 056038, China;The Modern Education Technology Center, Hebei University of Engineering, Hebei Handan 056038, China;The Modern Education Technology Center, Hebei University of Engineering, Hebei Handan 056038, China |
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| Abstract: | Bacterial Foraging Algorithm (BFA) is a new optimization algorithm based on the social foraging behavior of bacteria. It has been widely accepted as an optimization algorithm of current interest for distributed optimization and control to improve the adaptive performance of image enhancement, firstly, a king of adaptive chemotaxis factor of bacteria is employed to the optimization algorithm. Then the improved Adaptive Bacterial foraging algorithm (ABFA) is combined with the incomplete Beta function to obtain the optimum grey translation parameters. Finally, the degraded image is enhanced adaptively to the utmost extent. The simulation results show that the improved optimization algorithm is more efficient to refine parameters of the Beta function than its counterpart, thus enhancing the global contrast of the image and vision effectively. |
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| Keywords: | bacterial foraging algorithm image enhancement optimization algorithm chemotaxis factor |
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