基于自适应大气光值补偿的改进暗通道煤矿井下图像去雾算法(特邀)

Improved Dark Channel Defogging Algorithm for Underground Coal Mines Based on Adaptive Atmospheric Value Correction(Invited)

视频监控是保障工业生产和人员安全的重要手段。但在一些特殊领域,监控图像常会受到粉尘散射的影响而产生雾化现象、导致图像质量退化。此外,由于这些生产环境在照明、目标景物方面具有特殊性,常用的暗通道去雾算法存在色彩偏移、过暗、光晕等现象。以煤矿井下监控图像为目标,针对井下粉尘、水雾导致图像模糊、照度低、色彩信息不丰富的问题,提出一种改进的快速暗通道算法。该算法对大气光值进行基于像素自身颜色信息的自适应补偿校正,以减小色偏的产生,并针对井下图像提出了自拟合的亮度和饱和度增强函数。除对井下图像进行处理外,还将该算法用于常用去雾图像数据集,结果表明该算法对多种场景在有效去雾的同时很好地矫正了色偏及光晕现象,并提高了亮度和色彩饱和度。该算法为井工煤矿等产业以及生活场景的去雾提供了一种新的手段。

In industrial production,video surveillance is a key measure to maintain the industrial production and the safety of the employees.However,in some special fields,monitoring images are often affected by dust scattering and water mist scattering that leads to fogging and blurring,and results in degraded image quality,which influence the visibility of the visual observation.In addition,due to the specialty of lighting and the scenery in these working environments,commonly used dark channel dehazing algorithms have disadvantages,such as color shift,darkness,halo,etc.The traditional method is based on the atmospheric scattering model.It allocates the same value of the atmospheric light to all the pixel,which is suitable for the case of solar illumination,because the spectrum of sunlight is relatively close to the three channels of red green and blue.However,in special environments such as the presence of artificial light illumination,where the field of view and depth of field are limited,it is irrational to consider the atmospheric light of the image as the same value.Therefore,in the traditional algorithm,the dehazing image often results in color distortion,darkness,halo,etc.In order to solve these problems and better restore the image in the special environment,this paper focuses on monitoring images of underground coal mining scenery and proposes an improved fast dark channel algorithm to address these defects.This algorithm takes into account of the color pattern of the image and combines the dehazing algorithm with it to suppress the generation of color distortion.According to the theory of image color composition,the ratio of the Red,Green and Blue channels determines the hue of the pixel,and the changes of hue causes color distortion.So,to maintain the hue unchanged,it is necessary to control the ratio of the intensity values of the three channels.In this paper,the change of the pixel-based ratios between the three color channels before and after the processing of defogging with traditional dark channel prior method is analyzed to describe the generation of color distortion.Then based on this,the compensation method is proposed in which the atmospheric light value of each pixel is calculated and corrected according to the ratio of color channels in the input foggy image.Since the intensity distribution of the fog in the image is in the low frequency,the high-frequency and lowfrequency parts of the image are firstly separated and the dehazing processing is only applied to the lowfrequency part in order to preserve the image details.Besides this algorithm takes more measures to avoid the details of the image being destroyed,that is,the image of transmission and initial dark channel are processed by guided filtering,so that more details can be preserved.Furthermore,this algorithm proposes customized brightness and saturation enhancement functions for underground images that compensate low brightness and low saturation of the defogged images from traditional dark channel dehazing algorithm.In addition to mining images,this paper also applies the algorithm to common dehazing image datasets and compares it with some other algorithms.The comparison metric used in this article is structural similarity,which is a common measure for the similarity of two images.In addition,we also propose a new evaluation parameter,the color similarity,which is to detect the similarity between the dehazing image and the origin image in terms of color to give the evaluation of different methods on minimizing color distortion.The results show that the algorithm proposed in this paper can effectively remove fog,correct the color distortion and halo phenomenon,and improve the brightness and color saturation effectively.The algorithm provides a new means for defogging in industries such as mining and life scenarios.This algorithm provides a new idea for image processing in non-natural lighting and low brightness environments,and provides a guarantee for the smooth progress of underground production.