针对非完全Beta函数在图像增强过程中需手动调整参数、算法效率较低的问题,本文提出了一种基于改进麻雀搜索算法(LKSSA)的图像自适应增强方法(LKSSA-Beta)。首先,采用Logtistic混沌映射优化麻雀搜索算法(SSA)初始种群;其次,使用鸟群算法飞行行为思想及柯西高斯扰动提高SSA寻优能力;然后,利用LKSSA优化Beta函数的参数,构建灰度变换曲线,达到图像增强效果;最后,将本文算法与基于PSO图像增强法、基于人工蜂群图像增强法及基于传统Beta函数图像增强法实验结果进行对比。对比结果表明,LKSSA将具有更优的灰度图像全局搜索能力,本文算法可以保留图像更多细节信息,使图像整体对比度明显提高。
Abstract
Aiming at the problem that the parameters of incomplete Beta function need to be manually adjusted in the process of image enhancement and the algorithm efficiency is low,an adaptive image enhancement method (LKSSA-Beta) based on improved Sparrow Search algorithm(LKSSA) is proposed.Firstly,the initial population of sparrow search algorithm(SSA) is optimized by Logtistic chaotic mapping,maintaining population diversity.Next,the optimization ability of SSA is improved by using the flight behavior idea of bird swarm algorithm and Cauchy Gaussian disturbance.Then,LKSSA is used to optimize the parameters of the Beta function and construct the gray-scale transformation curve to achieve the image enhancement effect.Finally,the experiment results of LKSSA-Beta are compared with the image enhancement method based on PSO,the image enhancement method based on artificial bee colony,and the image enhancement method based on traditional Beta function.The results show that LKSSA will have better global search capabilities for grayscale images.LKSSA-Beta can preserve more detailed information of the image and make the overall contrast of the image more obvious.
关键词
自适应增强 /
麻雀算法 /
非完全Beta函数 /
Logistic映射 /
柯西高斯扰动
Key words
adaptive enhancement /
sparrow algorithm /
incomplete Beta function /
Logistic-map /
Cauchy and Gaussian disturbance
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基金
*陕西省技术创新引导专项(基金)计划(2019CGXNG-015)。
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