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Enhancement in Foggy Road Scene Videos Using RSWHE and Gamma Correction

机译:使用RSWHE和Gamma校正增强有雾道路场景视频

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摘要

Video enhancement is important for video security surveillance system because the videos and images of outdoor street scenes when captured in severe climatic conditions such as fog, dust storms, mist gets degraded. Drivers much of the time turn on the headlights of their vehicles and streetlights are frequently lit which decrease the visibility and leads to colour shift problems. Due to improper visibility it is difficult for drivers to identity the targets. The aim is to achieve haze free images/videos in order to detect the road conditions properly and increase visibility to reduce the road accidents. Various techniques are used for enhancement of videos/images such as Dark channel prior, Histogram equalization, Brightness preserving Bi histogram equalization, Recursively Separated and Weighted Histogram Equalization with Gamma Correction. In the proposed work, RSWHE is combined with gamma correction to improve the visibility of road scene foggy videos and images. Better results are obtained with the proposed method.
机译:视频增强对视频安全监控系统非常重要,因为在雾,沙尘暴,薄雾等恶劣气候条件下拍摄时,户外街道场景的视频和图像会退化。驾驶员大部分时间会打开车辆的大灯,并且路灯经常亮着,这会降低能见度并导致色偏问题。由于视野不当,驾驶员很难识别目标。目的是获得无雾的图像/视频,以正确检测道路状况并增加可视性以减少道路事故。各种技术被用于增强视频/图像,例如暗通道先验,直方图均衡,亮度保持Bi直方图均衡,递归分离和带有伽玛校正的加权直方图均衡。在拟议的工作中,RSWHE与伽玛校正相结合,以提高道路场景有雾视频和图像的可见性。所提出的方法获得了更好的结果。

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