Combining intelligent optimization and visual influence for image enhancement

doi:10.3778/j.issn.1002-8331.2011.03.059

Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (3): 199-201.DOI: 10.3778/j.issn.1002-8331.2011.03.059

• 图形、图像、模式识别 • Previous Articles Next Articles

Combining intelligent optimization and visual influence for image enhancement

HAN Quanye^1，2，WANG Haiyong²，WANG Xiaoming³，DANG Jianwu^2，3

1.Computer Information Management Department，Shaanxi Radio & TV University，Xi’an 710043，China
2.College of Electron and Information Engineering，Lanzhou Jiaotong University，Lanzhou 730070，China
3.Key Lab of Opto-Electronic Technology and Intelligent Control，MOE，Lanzhou Jiaotong University，Lanzhou 730070，China

Received:2009-04-27 Revised:2009-06-15 Online:2011-01-21 Published:2011-01-21
Contact: HAN Quanye

微粒群优化和视觉感应相结合的图像增强方法

韩泉叶^1，2，王海涌²，王晓明³，党建武^2，3

1.陕西广播电视大学计算机信息管理系，西安 710043
2.兰州交通大学电子与信息工程学院，兰州 730070
3.兰州交通大学光电技术与智能控制教育部重点实验室，兰州 730070

通讯作者: 韩泉叶

Abstract

Abstract: Particle Swarm Optimization（PSO） algorithm is improved，and the method about combining intelligent optimization and visual influence for image enhancement is proposed.By optimizing the difference of averag bright and dark information entropy of a gray image，the gray transformation function of an image is adaptively chosen.Lower parameters of this method are needed，higher optimizing speed of the method is possessed，searching ability of the method is superiority，and global convergence of the mothed is guaranteeed.The efficiency and superiority of this mothed can be confirmed by the simulation results.

Key words: information entropy, image enhancement, Particle Swarm Optimization（PSO）

摘要： 对粒群优化算法进行了改进，提出了一种微粒群优化和视觉感应相结合的图像增强方法，通过微粒群算法优化灰度图像的平均明暗信息熵差值，自适应地选择图像灰度转换函数，用以实现图像的增强。该方法不仅参数个数少，优化速度快，在搜索能力上优于粒群优化算法，而且能够保证算法的全局收敛性。仿真实例证明了该方法在图像增强上的有效性和优越性。

关键词: 信息熵, 图像增强, 粒群优化

CLC Number:

TP391

HAN Quanye^1，2，WANG Haiyong²，WANG Xiaoming³，DANG Jianwu^2，3. Combining intelligent optimization and visual influence for image enhancement[J]. Computer Engineering and Applications, 2011, 47(3): 199-201.

韩泉叶^1，2，王海涌²，王晓明³，党建武^2，3. 微粒群优化和视觉感应相结合的图像增强方法[J]. 计算机工程与应用, 2011, 47(3): 199-201.

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Combining intelligent optimization and visual influence for image enhancement

微粒群优化和视觉感应相结合的图像增强方法

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