摘要
提出了一种基因表达式编程寻优的色调对比度增强算法。选用多幅低照度图像作为参考图像,将该方法与自适应直方图均衡化、同态滤波、多尺度Retinex和带颜色恢复的多尺度Retinex等方法的实验结果进行了比较。所提算法的峰值信噪比、结构相似度和基于局部方差质量指数的平均值分别为25.93、0.75和0.87,均优于其他算法,在主观上亮度和对比度都更自然,更符合人眼视觉特性。该算法可广泛适用于低照度环境下的机器视觉领域。
A hue contrast enhancement algorithm based on gene expression programming optimization is proposed. A number of low illumination images are selected as the reference images and the results are compared with those of the adaptive histogram equalization, homomorphic filtering, multiscale Retinex enhancement, and color-restored multiscale Retinex enhancement. The average values of the peak signal-to-noise ratio, structural similarity and quality index based on local variance of the proposed algorithm are 25.93, 0.75, and 0.87, respectively, which are better than the other algorithms. Subjectively, the brightness and contrast of images processed by the propose method are more natural and more in line with human visual characteristics. The proposed algorithm can be widely applied to the field of machine vision in low illumination environments.
作者
贾新宇
李婷婷
江朝晖
刘海秋
饶元
Jia Xinyu;Li Tingting;Jiang Zhaohui;Liu Haiqiu;Rao Yuan(School of Information & Computer,Anhui Agricultural University, Hefei, Anhui 230036, China;Key Laboratory of Technology Integration and Application in Agricultural Internet of Things,Ministry of Agriculture, Hefei, Anhui 230036, China)
出处
《激光与光电子学进展》
CSCD
北大核心
2019年第9期187-193,共7页
Laser & Optoelectronics Progress
基金
国家重点研发计划(2018YFD0600101)
原农业部农业物联网技术集成与应用重点实验室开放基金(2016KL01)
关键词
机器视觉
图像增强
基因表达式编程
低照度
machine vision
image enhancement
gene expression programming
low illumination
