利用雾线进行空气光估计

Dana Berman, T. Treibitz, S. Avidan
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引用次数: 131

摘要

在恶劣天气条件下拍摄的户外图像,如雾霾和雾,看起来褪色,对比度降低。近年来,在单幅图像去雾方面取得了很大的成功,即从单幅图像中提高可视性和恢复颜色。这些方法的一个关键步骤是计算空气光颜色,即视线内没有物体的图像区域的颜色。我们提出了一种计算空气光的新方法。该方法依赖于最近引入的先验雾线。这一先验是基于这样的观察,即模糊图像的像素值可以建模为RGB空间中在空气光处相交的线。我们在RGB空间中使用霍夫变换来选择空气灯的位置。我们在现有的真实世界图像数据集,以及一些合成图像和其他真实图像上评估了所提出的方法。我们的方法与当前最先进的技术相当,并且计算效率更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Air-light estimation using haze-lines
Outdoor images taken in bad weather conditions, such as haze and fog, look faded and have reduced contrast. Recently there has been great success in single image dehazing, i.e., improving the visibility and restoring the colors from a single image. A crucial step in these methods is the calculation of the air-light color, the color of an area of the image with no objects in line-of-sight. We propose a new method for calculating the air-light. The method relies on the haze-lines prior that was recently introduced. This prior is based on the observation that the pixel values of a hazy image can be modeled as lines in RGB space that intersect at the air-light. We use Hough transform in RGB space to vote for the location of the air-light. We evaluate the proposed method on an existing dataset of real world images, as well as some synthetic and other real images. Our method performs on-par with current state-of-the-art techniques and is more computationally efficient.
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