背光
人工智能
计算机视觉
计算机科学
直方图均衡化
亮度
RGB颜色模型
直方图
自适应直方图均衡化
像素
颜色直方图
图像处理
彩色图像
图像(数学)
光学
物理
液晶显示器
操作系统
作者
Ronghua Ma,Zizhong Wei,Yixu He,Xin Huang,Mingxiao Ma
摘要
Due to the low foreground brightness and distorted background brightness of traffic backlight images, a technique of fusing LIME and improved histogram equalization is proposed to eliminate the backlight phenomenon in images. Firstly, the input image is segmented into two parts, foreground and background, using the maximum interclass variance method. Then LIME method is used globally for the backlit image to enhance the foreground brightness while maintaining the color distortion, and only the foreground part of the processed image is retained. Then the pixel value distribution of the background part is calculated separately, and the global histogram equalization results on the three RGB channels are mapped one by one to the corresponding limited interval, which improves the contrast of the background. Finally, the Canny operator is used to detect the black edges at the front background stitching, and three adaptive filtering templates are generated based on the black edges to perform step-by-step mean filtering on the black edges, eliminating the black edges and improving the visual quality of the image. The average metrics of the proposed method on the laboratory selfconstructed CHD_B dataset are, respectively, NIQE of 5.37, BRISQUE of 47.74, average gradient of 0.79, information entropy of 6.61, and average running time of 11.38s, which are synthetically better than the current backlight image enhancement methods and have better visual quality and visibility
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