摘要： 针对机器视觉图像清晰度在雾霾天气条件下受到严重影响的问题,提出一种基于暗原色先验的机器视觉图像去雾算法。以大气散射模型和暗原色先验理论为基础,在带雾图像的暗原色图上指定某一灰度区间,选取其中出现频率最高的亮度值作为大气光亮度值。将带雾图像转为灰度图像,采用直方图均衡化的方法对其进行增强,尽可能多地展现带雾图像所包含的结构信息,进而以增强后的灰度图像作为引导图像进行导向滤波,进一步优化透射率,加快运行速度。实验结果表明,该算法可获得较好的去雾效果,同时具有较高的运算效率。

关键词: 机器视觉, 暗原色先验, 大气光估计, 直方图均衡化, 导向滤波, 去雾

Abstract: Aiming at restrictions of machine vision image under fog and haze weather conditions,a machine vision image defogging algorithm is proposed based on dark channel prior.It is based on atmospheric scattering model and dark channel prior principle.It uses a new method to estimate the atmospheric brightness by specifying a gray zone in the dark channel chart of fog image and then selects the brightness of the highest frequency as the atmospheric brightness.Then it converts the fog image is converted to grayscale image and the histogram equalization method is used to enhance it.So that the structural information contained in the fog image can be shown as much as possible.The running time is reduced by using guided filtering to optimize the transmission.Experimental results show the proposed algorithm can get better defogging effects and improve operational efficiency.

Key words: machine vision, dark channel prior, atmospheric light estimation, histogram equalization, guided filtering, defogging

中图分类号: 

• TP391