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Video Image Clarity Algorithm Research of USV Visual System under the Sea Fog

机译:海洋雾下USV视觉系统的视频图像清晰算法研究

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The visual system is one of the main equipment of unmanned surface vehicle (USV) autonomous navigation. Under the sea fog, atmospheric particles scattering leads to serious image degradation of the visual system. Because there is obvious sea-sky-line and the larger sky area in the image of offshore, so firstly, the image segmentation is done to get sky area, and through anglicizing sky area characteristics, the sky brightness is estimated, and then a simplified physical model of atmospheric scattering is built up, lastly image scene recovery is finished. Thinking about using this simple image defogging method to video image, foreground and background separation is done. Comparative research with several defogging methods onshore, results show that the proposed method can enhance the video image clarity of the USV visual system under sea fog very well. This research brought a good foundation to further improve the accuracy and precision of surface target identification and tracking algorithm.
机译:视觉系统是无人面车辆(USV)自主导航的主要设备之一。在海雾下,大气颗粒散射导致视觉系统的严重图像劣化。因为有明显的海洋线和近海的图像中的较大的天空区域,所以首先,进行图像分割以获得天空区域,通过抗刺激天空区域特征,估计天空亮度,然后是简化的建立了大气散射的物理模型,最后图像场景恢复完成。考虑使用这种简单的图像Defogging方法来视频图像,前景和背景分离。具有几种缺陷方法的比较研究,结果表明,所提出的方法可以很好地增强海洋雾下USV视觉系统的视频图像清晰度。这项研究带来了良好的基础,以进一步提高表面目标识别和跟踪算法的准确性和精度。

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