重庆大学学报2025，Vol.48Issue(4)：115-126,12.DOI:10.11835/j.issn.1000-582X.2025.04.010
回归拟合NR函数及GPDR先验的图像雾浓度检测
Inspection of image fog Concentration using regression-fitting NR function and GPDR prior
摘要
Abstract
Addressing the limitations of fog concentration inspection in image defogging,an algorithm based on the scatterplot prior of the generalized pixel difference-ratio(GPDR)and the Naka-Rushton(NR)fitting function was proposed.First,the GPDR prior for gray scatterplots in standard foggy image sets across various scenes was extracted.Next,the NR function,constrained by the prior,was introduced,and a lookup table of parameters(n,k)corresponding to fog concentration levels was established by calculating the parameters(n,k)of NR function for standard image sets.Regression analysis was then used to calculate the parameters(n',k')for real foggy images,and the comprehensive correlation coefficient between(n,k)and(n',k')was calculated.Parameters(n,k)with correlation coefficients exceeding a set threshold were considered indicative of the fog concentration level.Simulations show that the algorithm accurately reflect changes in fog concentration across images with varying densities.Additionally,correlation coefficients between the algorithm's results and PM2.5 measurements reached up to 0.95,both within the same and across different scenes.This shows that the algorithm can be effectively used for fog concentration rating in visual field.Horizontal comparison tests show that the inspection accuracy of the proposed algorithm can reach up to 4.8%,making it suitable for field fog concentration detection.关键词
雾浓度/LIVE库/灰度差-比先验/Naka-Rushton函数/检测Key words
fog concentration/LIVE library/GPDR/Naka-Rushton function/inspection分类
计算机与自动化引用本文复制引用
温立民,王会峰,巨永锋..回归拟合NR函数及GPDR先验的图像雾浓度检测[J].重庆大学学报,2025,48(4):115-126,12.基金项目
国家自然科学基金(52172324) （52172324）
陕西省交通厅重点项目(20-38T).Supported by National Natural Science Foundation of China(52172324)and Key Project of Shaanxi Provincial Department of Communications(20-38T). （20-38T）
