摘要
针对由于煤矿井下环境存在大量煤尘、水雾,监控图像出现模糊、退化现象的问题,提出一种基于暗原色原理和主成分分析的煤矿井下雾尘图像清晰化算法。该算法基于大气散射模型,根据暗原色原理计算透射率;用主成分分析法得出能够充分反映雾尘图像信息的亮度、饱和度及对比度,通过对这些指标进行加权处理来计算大气光值,实现了对煤矿井下雾尘图像的清晰化处理。仿真结果表明,该算法可较大程度地还原图像细节,并保持图像的真实性和结构完整性,实时性较好。
In view of fuzzy and degenerated images in coal mine environment due to presence of large amounts of coal dust and water mist,a sharpening algorithm based on dark primary principle and principal component analysis was proposed.Based on atmospheric scattering model,transmittance is calculated according to the dark primary principle.The principal component analysis is used to obtain brightness,saturation and contrast,which can fully reflect fog image information.Atmospheric light value is calculated by weighting these indexes,so as to realize sharpening process of underground images with fog and dust in underground coal mine.The simulation results show that the proposed algorithm can restrain image detail to a great extent,maintain authenticity and structural integrity of the image,and have good real-time performance.
作者
吴开兴
张琳
李丽宏
WU Kaixing;ZHANG Lin;LI Lihong(Hebei Provincial Engineering Laboratory of Coal Mine Comprehensive Information, Handan 056038,China;College of Information and Electrical Engineering,Hebei University of Engineering,Handan 056038,China)
出处
《工矿自动化》
北大核心
2018年第3期70-75,共6页
Journal Of Mine Automation
基金
河北省教育厅项目(ZD2014081)
河北省自然科学基金资助项目(F2015402150)
关键词
井下视频监控
雾尘图像
清晰化算法
暗原色原理
主成分分析
underground video surveillance
fog and dust image
sharpening algorithm
dark primary principle
principal component analysis
