通过基于像素的暗通道先验的雾霾密度分析实现高效的图像/视频去雾

C. Yeh, Li-Wei Kang, Cheng-Yang Lin, Chih-Yang Lin
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引用次数: 36

摘要

室外场景的图像/视频通常会因大气中的浑浊介质而退化。本文提出了一种新的基于单图像的去雾框架来去除图像/视频中的雾霾效果,其中我们提出了两种新的图像先验,称为基于像素的暗通道先验和基于像素的亮通道先验。在此基础上,结合雾霾成像模型,提出了通过雾霾密度分析来准确估算大气光的方法。然后,我们可以准确地估计传输图,然后通过双边滤波器对其进行细化。因此,可以以较低的计算复杂度恢复高质量的无雾图像,可以自然地扩展到视频去雾。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient image/video dehazing through haze density analysis based on pixel-based dark channel prior
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.
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