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UAV Aerial Image Defogging based on Optimum of Quality Evaluation

机译:基于质量评价最佳的UAV空中图像缺陷

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Unmanned aerial vehicles have been widely used in military and civil areas, which requires vision processing in explicit usage scenario. Existence of haze or fog can influence the context awareness capability of the aerial vehicles and makes affectation on target tasks. The captured images in hazy scenes suffer from degradation problems including poor contrast, color distortion, incomplete information, which lead to many difficulties in the follow-up processing. A simple and effective single image dehazing algorithm based on atmospheric scattering model and the optimum of image quality evaluation is proposed in this paper. Three image quality evaluation parameters: image entropy, standard deviation, and Fourier amplitude are combined to establish and the image quality evaluation function. On the basis of quality evaluation function, the image with the optimum of quality evaluation among the potential defogging images is chosen as the best result. Results show that this method has lower computational complexity, simplified operations and improved real-time performance.
机译:无人驾驶航空公司已广泛应用于军事和民用地区,这需要在明确的使用情况下进行视觉处理。雾霾或雾的存在可以影响空中车辆的上下文意识能力,并对目标任务进行影响。在朦胧场景中捕获的图像遭受了劣化问题,包括差的对比度,颜色失真,信息不完整,这导致后续处理的许多困难。本文提出了一种基于大气散射模型的简单有效的单幅图像去吸收算法及其图像质量评价的最优。三个图像质量评估参数:组合图像熵,标准偏差和傅立叶幅度建立和图像质量评估功能。在质量评价功能的基础上,选择具有最佳质量评估的图像作为最佳结果。结果表明,该方法具有较低的计算复杂性,简化的操作和改进的实时性能。

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