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Efficient image/video dehazing through haze density analysis based on pixel-based dark channel prior

机译:通过基于像素的暗通道先验的雾度密度分析实现有效的图像/视频除雾

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摘要

Images/videos of outdoor scenes are usually degraded by the turbid medium in the atmosphere. In this paper, a novel single image-based dehazing framework is proposed to remove haze effects from image/video, where we propose two novel image priors, called the pixel-based dark channel prior and the pixel-based bright channel prior. Based on the two priors with the haze imaging model, we propose to accurately estimate the atmospheric light via haze density analysis. We can then accurately estimate the transmission map, followed by refining it via the bilateral filter. As a result, high-quality haze-free image can be recovered with lower computational complexity, which can be naturally extended to video dehazing.
机译:室外场景的图像/视频通常会被大气中的混浊介质降解。在本文中,提出了一种新颖的基于单个图像的除雾框架,以消除图像/视频中的混浊效应,在此我们提出了两种新颖的图像先验,分别称为基于像素的暗通道先验和基于像素的亮通道先验。基于雾度成像模型的两个先验,我们建议通过雾度密度分析准确估算大气光。然后,我们可以准确估计传输图,然后通过双边滤波器对其进行完善。结果,可以以较低的计算复杂度恢复高质量的无雾图像,并且可以自然地扩展到视频去雾。

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