回歸擬合NR函數(shù)及GPDR先驗的圖像霧濃度檢測

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中圖分類號：TP391.9 文獻(xiàn)標(biāo)志碼：A 文章編號：1000-582X（2025）04-115-12

Inspection of image fog Concentration using regression-fitting NR function and GPDR prior

WEN Limin*b， WANG Huifenga， JU Yongfenga （a.School of Electronic & Engineering； b. Experimental Teaching Center of Electronics and Electronics， Chang'an University， Xi'an 710064， P.R.China）

Abstract：Addressing the limitations of fog concentration inspection in image defogging，an algorithm based on the scatterplot prior of the generalized pixel diference-ratio（GPDR）and the Naka-Rushton（NR）fitting function was proposed.First，the GPDR prior for gray scaterplots 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 for real foggy images， and the comprehensive correlation coefficient between （n，k） and was calculated. Parameters （n，k） with correlation coeffcients 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 andacrossdiffrent scenes.This shows thatthealgorithm 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. Keywords： fog concentration； LIVE library； GPDR； Naka-Rushton function； inspection

霧是生活中常見的自然現(xiàn)象，其存在會影響人們的生產(chǎn)和生活，通常這種影響是負(fù)面的，需要減緩或消除。（剩余12581字）