Intelligent Video Defogging Technology Based on Covariance and Perceptron

摘要：

As the algorithm of video defogging must meet realtime requirement, a new method is proposed, it uses the covariance matrix of multi-feature combination to describe image features, and combines with perceptron model to intelligently detect foggy scenes. Because the backgrounds of industrial video images generally change slowly, Gaussian mixture modeling is used to get foregrounds. The transmission of dark channel prior is updated according to the foreground. Then each frame is restored directly according to the newer transmission. The defogging algorithm greatly reduces the running time. It achieves the purpose of video defogging. Experimental results show that the algorithm has a high accuracy on detecting foggy scenes. The algorithm of video defogging proposed can meet the industrial real-time requirements and ensure spatial and temporal consistency of video.

关键词： video defogging multi-feature combination covariance dark channel prior perceptron

作者: LongliLi QingLiu Jianming Guo Yanfan Xiong

作者单位: School of Automation Wuhan University of Technology Wuhan, Hubei, PRC

会议类型: 国际会议

会议名称: 2011 Seventh International Conference on Natural Computation(第七届自然计算国际会议 ICNC 2011)

会议地点: 上海

会议语种:英文

页码: 166-170

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）