雾密度估计和基于暗通道先验的图像去雾

Rujun Li, U. KinTak
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引用次数: 3

摘要

在各种去雾算法中,暗通道先验去雾算法是一种简单有效的去雾算法。暗通道先验的缺点包括在天空和水面等明亮区域存在一定程度的色彩失真。针对这一问题,提出了基于无参考预测感知雾密度模型的改进算法——雾感密度评估器(fog - Aware density Evaluator, FADE),以更精确地估计明亮区域的大气光A和介质透射,避免天空区域的色移。本文还采用了快速引导滤波来细化介质传输。实验结果表明,本文提出的改进算法得到的恢复图像的天空区域没有出现严重的色彩失真问题,并且比暗通道先验去雾算法更有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Haze Density Estimation and Dark Channel Prior Based Image Defogging
Among various de-fog algorithms, dark channel prior de-fog algorithm is one of simple and effective dehazing algorithm. The disadvantages of dark channel prior include that there is a certain degree of color distortion in bright areas such as sky and water surface. Aiming at the problem, improved algorithm based on reference-less prediction of perceptual fog density model, Fog Aware Density Evaluator (FADE) is introduced to get more exact estimation of atmospheric light A and medium transmission in the bright areas to avoid the color shift in the sky region. Fast guided filtering is also used in this paper to refine the medium transmission. The result of experiment shows that there is no serious color distortion problem in the sky region of the restored image obtained by the improved algorithm proposed in this paper and the algorithm is more effective than dark channel prior de-fog algorithm.
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