Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (5): 1435-1438.DOI: 10.11772/j.issn.1001-9081.2015.05.1435

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Enhancement algorithm for fog and dust images in coal mine based on dark channel prior theory and bilateral adaptive filter

DU Mingben, CHEN Lichao, PAN Lihu   

  1. School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan Shanxi 030024, China
  • Received:2014-12-15 Revised:2015-01-23 Online:2015-05-10 Published:2015-05-14

基于暗原色理论和自适应双边滤波的煤矿尘雾图像增强算法

杜明本, 陈立潮, 潘理虎   

  1. 太原科技大学 计算机科学与技术学院, 太原 030024
  • 通讯作者: 杜明本
  • 作者简介:杜明本(1990-),男,山东济南人,硕士研究生,主要研究方向:图像处理、计算机视觉; 陈立潮(1961-),男,山西万荣人,教授,博士,主要研究方向:计算智能、群体智能、智能软件技术; 潘理虎(1974-),男,山西太原人,副教授,博士,主要研究方向:软件体系结构、软件工程、复杂系统建模.
  • 基金资助:

    山西省科技重大专项(20121101001);山西省科技攻关项目(20141039);山西省留学人员科研资助项目(2013-097).

Abstract:

Concerning the problem that videos images captured from coal mines filled with coal dust and mist are often with quality problems such as lots of noise, low resolution and blur. To solve this problem, an enhancement algorithm for fog and dust images in coal mine based on dark channel prior theory and bilateral adaptive filter was proposed. On the basis of dark channel prior, the softmatting process was replaced with the adaptive bilateral filtering to obtain fine transmittance map. Then according to the special circumstances of coal mines, the global atmosphere light and the rough transmittance map were got from new perspective and image denoising was realized on the basis of the image degradation model. The experiment results show that the image processing time for a resolution of 1024×576 is 1.9 s. Compared with He algorithm (HE K, SUN J, TANG X. Single image haze removal using dark channel prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011,33(12):1-13.), the efficiency increased 5 times.Compared with other algorithms such as histogram equalization method, the proposed algorithm is effective to enhance the image detail. In this way, images can be more suitable for human vision as a whole.

Key words: dark channel prior theory, bilateral adaptive filtering, fog removing of image, medium transmission function, atmospheric scattering model

摘要:

针对煤矿井下存在大量煤尘、水雾导致获取的视频图像伴有大量的噪声、分辨率低、模糊的问题,提出了一种基于暗原色理论和自适应双边滤波的煤矿尘雾图像增强算法.基于暗原色先验理论,采用自适应双边滤波代替softmatting过程来求取精细透射率图,并根据煤矿井下特殊环境,从新的角度求取全球大气光值、粗略透射率图,并根据图像退化模型实现图像的去噪.实验结果表明,对于分辨率为1024×576的图像处理时间为1.9s,与He算法(HE K, SUN J, TANG X. Single image haze removal using dark channel prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011,33(12):1-13.)相比,运行效率提高了5倍.与直方图均衡法等算法相比,所提算法有效增强了图像细节、边缘,整体上更加适合人类视觉和视频监控的要求.

关键词: 暗原色先验理论, 自适应双边滤波, 图像去雾, 介质传播函数, 大气物理散射模型

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