| 基于人工鱼群优化分块的多聚焦图像融合 |
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| 引用本文： | 费春,张萍,李建平. 基于人工鱼群优化分块的多聚焦图像融合[J]. 强激光与粒子束, 2015, 27(1): 011012. DOI: 10.11884/HPLPB201527.011012 |
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| 作者姓名： | 费春 张萍 李建平 |
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| 作者单位： | 1.电子科技大学 计算机学院, 成都 61 0054; |
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| 基金项目： | 国家自然科学基金项目(61308102)； 四川省科技计划基金项目(2014GZ0005, 2014JY0228)； 中国博士后科学基金项目(2013M531946); 高等学校博士学科点专项科研基金项目(A0901050130185120038)； 中央高校基本科研业务费专项资金项目(ZYGX2013J059) |
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| 摘 要： | 在多聚焦图像的融合过程中,对源图像采用固定大小的分块会导致融合后的图像存在块效应、边缘模糊甚至聚焦错误。为了克服此问题,提出了一种新的基于人工鱼群优化分块的多聚焦图像融合方法。首先,将源图像分解成互不重叠的方块,利用聚焦准则选取清晰度高的方块,将已选择的方块合并重构成初始融合图像。然后,利用改进的人工鱼群优化算法,根据一定的适应度值,寻找最优大小的分块方式,获得更优的融合图像。该方法与基于空域、频域及其他优化算法的融合方法进行了多个实验比较,结果表明,该方法获得的融合图像具有较好的客观质量和主观视觉感觉。
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| 关 键 词： | 多聚焦 人工鱼群优化 图像融合 聚焦准则 适应度值 |
| 收稿时间： | 2014-07-17 |
Multi-focus images fusion based on block optimization using artificial fish-swarm algorithm |
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| Fei Chun , Zhang Ping , Li Jianping. Multi-focus images fusion based on block optimization using artificial fish-swarm algorithm[J]. High Power Laser and Particle Beams, 2015, 27(1): 011012. DOI: 10.11884/HPLPB201527.011012 |
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| Authors: | Fei Chun Zhang Ping Li Jianping |
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| Affiliation: | 1.School of Computer Science &Engineering,University of Electronic Science and Technology of China,Chengdu 610054,China;2.School of Optoelectronic Information,University of Electronic Science and Technology of China,Chengdu 610054,China |
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| Abstract: | The fixed block size of source images will result in blocking artifacts, fuzzy edge and focus error in multi-focus image fusion. To solve this problem, a new multi-focus image fusion algorithm based on block optimization using artificial fish-swarm is proposed. Firstly, the source images are decomposed into non-overlapping blocks and the sharper blocks are selected using a sharpness criterion. The selected blocks are combined to construct the initial fused image. Then, an improved artificial fish-swarm algorithm is used to optimize the block size according to a fitness function. The final fused image is obtained based on the best block size. Experimental results show that the proposed fusion method has a good quantitative evaluation and visual effect compared to other traditional methods. |
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| Keywords: | multi-focus artificial fish-swarm image fusion sharpness criterion fitness function |
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