摘要
针对现有低光照图像增强算法存在对比度低、边缘细节丢失及增强过度等问题,提出一种基于全局双伽马校正与改进SSA算法结合的低光照图像增强方法。首先对图像预处理,其次采用双伽马函数结合麻雀优化算法进行全局校正,此外为改善算法收敛性能,引入精英反向学习和Lévy飞行策略来改进麻雀算法,优化对参数(α)的选择,通过寻找最优伽马值实现对图像的细节增强。仿真实验结果表明,该算法增强后的图像峰值信噪比和结构相似性指标较大,图像颜色失真较小,细节更加丰富,整体增强效果优于其他对比算法,具有较好的处理效果。
To address the problems of low contrast,edge detail loss and excessive enhancement in existing low-light image enhancement algorithms,a low-light image enhancement method based on the combination of global double gamma correction and improved SSA algorithm is proposed.In addition,to improve the convergence performance of the algorithm,elite backward learning and Lévy flight strategy are introduced to improve the sparrow algorithm,optimize the selection of parameter(α),and realize the detail enhancement of the image by finding the optimal gamma value.The simulation experimental results show that the algorithm enhances the image with larger peak signal-to-noise ratio and structural similarity index,less image color distortion,and sharpens the edges,and the overall enhancement effect is better than other comparison algorithms,which has better processing effect.
作者
田柯
马小晶
贺航
Tian Ke;Ma Xiaojing;He Hang(School of Electrical Engineering,Xinjiang University,Urumqi 830049,China)
出处
《电子测量技术》
北大核心
2023年第9期124-133,共10页
Electronic Measurement Technology
基金
新疆维吾尔自治区“天山英才”培养计划(2022TSYCCX0054)
新疆维吾尔自治区重大科技专项(2022A01002-2)
国家自然科学基金(12002296)
新疆维吾尔自治区自然科学基金(2022D01C47)项目资助。
关键词
图像增强
精英反向学习
双伽马校正
Lévy飞行策略
麻雀搜索算法
image enhancement
elite backward learning
double gamma correction
lévy flight strategy
sparrow search algorithm
