基于人工蜂群优化的自适应图像增强方法
2020-10-23
作者：王延年，程燕杰，钟正，李文婷，李雄飞
单位：西安工程大学
摘要：结合人工蜂群优化算法和非完全Beta函数，将人工蜂群算法作为优化问题寻找最优解，提出基于人工蜂群优化的自适应图像增强算法。通过人工蜂群优化算法并行搜索非完全Beta函数的最佳参数，从而确定最优的灰度变换曲线；引入适应度函数来指导人工蜂群的搜索运动，以此实现图像对比度的自适应增强。仿真试验结果表明：与直方图均衡化、反锐化掩模算法相比较，本算法的灰度分布更加均匀，对比度明显，视觉效果更好且适用性强。
关键词：工蜂群优化算法；对比度增强；非完全Beta函数；适应度函数
中图分类号：TG702；TH164；TP391文献标志码：ADOI：10.3969/j.issn.1000-7008.2020.08.019
Adaptive Image Enhancement Method Based on Artificial Swarm Optimization
Wang Yannian，Cheng Yanjie，Zhong Zheng，Li Wenting，Li Xiongfei
Abstract:Based on artificial colony optimization，an adaptive image enhancement algorithm is proposed by combining artificial colony optimization algorithm with incomplete Beta function.The artificial bee colony algorithm as an optimization problem to find the optimal solution is taken.The optimal gray scale transformation curve is determined by the parallel search of the optimal parameters of the incomplete Beta function by the artificial bee colony optimization algorithm.The fitness function is introduced to guide the search movement of the artificial bee colony，so as to achieve the adaptive enhancement of image contrast.Simulation results show that compared with histogram equalization and antisharpening mask algorithm，the gray distribution of the algorithm is more uniform，the contrast is obvious and the visual effect is better and more suitable.
Keywords:artificial swarm optimization algorithm；contrast enhancement；incomplete Beta function；fitness function