| 一种基于混合人工鱼群的图像模糊增强算法 |
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| 引用本文： | 杨松,邵龙潭,张菁,林刚,包琳.一种基于混合人工鱼群的图像模糊增强算法[J].应用声学,2015,23(2). |
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| 作者姓名： | 杨松 邵龙潭 张菁 林刚 包琳 |
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| 作者单位： | 大连理工大学 工业装备结构分析国家重点实验室,大连理工大学 工业装备结构分析国家重点实验室,大连海洋大学 教育技术与计算中心,92538部队,大连海洋大学 教育技术与计算中心 |
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| 基金项目： | 国家973计划项目(2010CB7315022)；国家自然科学基金（50905022）；工业装备结构分析国家重点实验室专项基金项目(S09104)；中央高校基本科研业务费专项资金（DUT12LK21）; |
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| 摘 要： | 模糊隶属度函数的形式直接影响灰度图像增强的质量。为进一步改善图像模糊增强的效果,对目前的模糊隶属度函数进行研究,并提出一种改进的参数化 型模糊隶属度函数用于图像增强。所提算法利用图像对比度的质量评价模型,结合人工鱼群算法和Powell算法搜索 型函数中的未知参数值,进而确定该模糊隶属度函数。通过实验结果表明：该算法能够较好的改善灰度图像质量,并且控制参数可通过优化算法自适应获得,具有较好的通用性,是一种有效的图像模糊增强算法。
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| 关 键 词： | 图像增强 人工鱼群 Powell 模糊隶属度函数 灰度对比度 |
| 收稿时间： | 7/1/2014 12:00:00 AM |
| 修稿时间： | 8/3/2014 12:00:00 AM |
AN IMAGE FUZZY ENHANCEMENT ALGORITHM BASED ON HYBRID ARTIFICIAL FISH SWARM OPTIMIZATION |
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| Shao Longtan,Zhang Jing,Lin Gang and Bao Lin.AN IMAGE FUZZY ENHANCEMENT ALGORITHM BASED ON HYBRID ARTIFICIAL FISH SWARM OPTIMIZATION[J].Applied Acoustics,2015,23(2). |
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| Authors: | Shao Longtan Zhang Jing Lin Gang and Bao Lin |
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| Institution: | Dalian University of Technology: State Key Laboratory of Structural Analysis of Industrial Equipment,Dalian University of Technology: State Key Laboratory of Structural Analysis of Industrial Equipment,Dalian Ocean University: Education Technology & Computing Center,92538 PLA Troops,Dalian Ocean University: Education Technology & Computing Center |
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| Abstract: | The form of fuzzy membership function directly affects the quality of gray-level image enhancement. To further improve the effect of image fuzzy enhancement, the study on the current fuzzy membership function is done, and a parameterized S-type fuzzy membership function is proposed for image enhancement. The proposed algorithm uses the gray-level contrast as image quality evaluation model, and searches the unknown parameters by combining with artificial fish swarm algorithm and Powell algorithm, and thus the fuzzy membership function can be determined. The experimental results show that the proposed algorithm can be used to improve image quality and has higher versatility because all the parameters can be obtained adaptively by optimization, and proved to be an effective algorithm of image fuzzy enhancement. |
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| Keywords: | Image enhancement Artificial fish swarm Powell Fuzzy member function Gray-level contrast |
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