改进的自适应遗传算法在图像模糊增强中的应用  

Application of improved adaptive genetic algorithms in image fuzzy enhancement

作　　者：李国[1,2] 龚志辉[1] 尤辉 张一平[1] 靳克强[1,3] 王勃[1] 

机构地区：[1]信息工程大学测绘学院 [2]65015部队 [3]96633部队

出　　处：《测绘科学》2012年第2期77-79,共3页Science of Surveying and Mapping

摘　　要：本文在图像增强中应用了模糊集理论,实现了以空间域—模糊域—空间域为主要过程的的图像模糊增强。首先通过Fibonacci数列对遗传算法做了自适应改进,而后以改进的适应度函数为惟一内在驱动力,利用遗传算法对模糊参数进行了自动选择,最后与传统的线性增强、直方图均衡进行了对比实验。实验结果表明该方法能改善原图像视觉效果,便于后续的图像分析。The theory of fuzzy congregation was used in image enhancement in the paper, image enhancement was realized by the process from space-domain to fuzzy field then turn over. The first, an improving adaptive Genetic Algorithms was finished by Fibonacci sequence. The second, Genetic Algorithms was used to select optimum fuzzy parameter through the improved fitness function as the only inherent driving force. The last, comparative experiment was made among the method, traditional linear enhancement and histogram equalization. The results showed that the method discussed could improve vision of image and be convenient for image analysis.

关 键 词：图像增强 遗传算法 模糊熵 FIBONACCI数列 GRAY码 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]