基于滚动时域粒子群优化的视频去雾算法

Receding horizon particle swarm optimization based video defogging algorithm

无人机视频由于拍摄的位置和场景不断移动,环境参数亦不断变化,采用以往针对固定场景的去雾方法不能达到最佳效果.为了使无人机视频去雾算法具有自适应性,提出一种基于滚动时域粒子群优化的视频去雾算法.将基于周期和事件混合驱动的滚动调度策略与粒子群算法(PSO)相结合,对可调去雾参数进行滚动优化调整,当与上次优化的帧间隔数大于阈值或环境和场景发生改变时,启动粒子群优化算法重新选取最佳去雾参数.针对无人机视频,分别应用所提出算法和固定去雾参数算法进行实验和对比分析,实验结果表明,对于环境因素动态变化的视频,所提出算法比固定去雾参数算法具有更好的对比度和视觉效果.

The defogging algorithm developed for fix scenes can not achieve the best effects for videos shot by unmanned aerial vehicles(UAVs) since the locations, scenes and environments of UAVs keep varying. The video defogging algorithm based on receding horizon particle swarm optimization is proposed for adaptive defogging. The defogging parameter can be optimized by combining the particle swarm optimization(PSO) algorithm and the receding horizon strategy driven by time and events. The PSO algorithm is started to optimize the defogging parameter when environment or scene changes,or frame interval is over the threshold. The proposed video defogging algorithm is verified in experiments and compared with the fixed parameter video defogging algorithm for UAV videos. The experimental results show that the proposed algorithm has better contrast and visual effect than the fixed parameter algorithm for videos with environment variations.