一种采用改进蚁狮优化算法的图像增强方法

摘要

针对现有图像增强技术在细节处理方面的不足以及变换后图像直方图分布偏移的情况,同时针对基本的蚁狮优化算法(ALO)存在寻优精度不理想、易陷入局部最优等问题,提出一种采用改进蚁狮优化算法(RBALO)的图像增强方法.其核心在于,根据蚁狮位置的分布调整搜索空间的边界,引导蚂蚁在更有效的区域内进行搜索,并使部分蚂蚁与精英蚁狮进行重组.将RBALO算法用于确定Beta函数的参数,获取适合当前图像的参数值.利用两个标准测试函数进行实验,证明改进后的蚁狮算法有非常好的寻优精度.最后,使用couple灰度图进行图像仿真实验,结果表明使用RBALO算法得到的增强图像适应度优,直方图分布更均匀.%In view of the shortcomings of the existing image enhancement technology in detail processing and the situation that the histogram distribution of the transformed image is shifted.At the same time,it is not ideal for the basic antlion optimization algorithm (ALO),easy to fall into the local optimum and so on,This paper proposed an image enhancement method using improved antlion optimization algorithm (RBALO).It adjusted the boundary of the search space according to the distribution of the antlion location,guided the ants to search in more efficient areas.Finally,let parts of ants and the elite antlion position reorganization.It used the RBALO algorithm to determine the parameters of the Beta function,got the value of the parameter appropriate for the current image.It used two standard test functions for testing and it was proved that the improved antlion algorithm had very good precision.Finally,using couple grayscale image simulation experiments,the experimental results show that the RBALO algorithm has better fitness and more uniform histogram distribution.