张征明, 卢伟, 陆静霞. 基于快速伪球滤波的智能拖拉机视觉导航中场景去雾方法[J]. 机器人, 2015, 37(5): 603-613,640. DOI: 10.13973/j.cnki.robot.2015.0603
引用本文: 张征明, 卢伟, 陆静霞. 基于快速伪球滤波的智能拖拉机视觉导航中场景去雾方法[J]. 机器人, 2015, 37(5): 603-613,640. DOI: 10.13973/j.cnki.robot.2015.0603
ZHANG Zhengming, LU Wei, LU Jingxia. Dehazing Method Based on Fast Pseudosphere Filtering for Visual Navigation of Intelligent Tractor in Haze Weather Scene[J]. ROBOT, 2015, 37(5): 603-613,640. DOI: 10.13973/j.cnki.robot.2015.0603
Citation: ZHANG Zhengming, LU Wei, LU Jingxia. Dehazing Method Based on Fast Pseudosphere Filtering for Visual Navigation of Intelligent Tractor in Haze Weather Scene[J]. ROBOT, 2015, 37(5): 603-613,640. DOI: 10.13973/j.cnki.robot.2015.0603

基于快速伪球滤波的智能拖拉机视觉导航中场景去雾方法

Dehazing Method Based on Fast Pseudosphere Filtering for Visual Navigation of Intelligent Tractor in Haze Weather Scene

  • 摘要: 为适应智能拖拉机雾霾天气下道路的视觉导航,同时针对作业环境图像背景中天空居多的特征,提出一种基于快速伪球滤波的去雾方法.首先根据滤波器参数利用降秩逼近的方法对视觉图像产生滤波模板.而后利用伪球滤波保持边缘的平滑特性获得准确的暗通道图和大气透射图.最后,利用大气耗散模型的修正解快速恢复无雾图像.实验结果表明,快速伪球滤波方法与导向图滤波、Tarel 中值滤波、多尺度 Retinex、小波域 Retinex 方法相比,对图像去雾效果综合评价指标值分别提高 54.7%、37.6%、35.2%、44%,算法耗时约 0.18s.能够满足智能拖拉机视觉导航实时性的要求.

     

    Abstract: In order to make the visual navigation of intelligent tractors adapt to the foggy weather, an image dehazing method using the fast pseudosphere filter is proposed, to get a clear image from a foggy picture with the sky as main background.Firstly, the filter template for the visual images is obtained based on filter parameters by using the reduced rank approximation method.Then, the edge smoothness is guaranteed through pseudosphere filtering to get the exact dark channel and the atmosphere scattering veil.Finally, a clear view of the image is recovered quickly with the correction solution of the atmospheric dissipation model.The experimental results show that this method can improve the objective assessment indicator of the image dehazing by 54.7%, 37.6%, 35.2% and 44% compared with the guided image filtering method, Tarel median filtering method, multi-scale retinex method and the wavelet-based retinex method.Furthermore, the time consumption of this algorithm is about 0.18s, which can meet requirements of the intelligent tractors for real-time visual navigation.

     

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