IEEE Trans Pattern Anal Mach Intell. 2011 Dec;33(12):2341-53. doi: 10.1109/TPAMI.2010.168. Epub 2010 Sep 9.
In this paper, we propose a simple but effective image prior-dark channel prior to remove haze from a single input image. The dark channel prior is a kind of statistics of outdoor haze-free images. It is based on a key observation-most local patches in outdoor haze-free images contain some pixels whose intensity is very low in at least one color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high-quality haze-free image. Results on a variety of hazy images demonstrate the power of the proposed prior. Moreover, a high-quality depth map can also be obtained as a byproduct of haze removal.
在本文中,我们提出了一种简单而有效的图像先验——暗通道先验,用于从单个输入图像中去除雾霾。暗通道先验是一种户外无雾图像的统计信息。它基于一个关键观察——大多数户外无雾图像的局部斑块都包含一些像素,这些像素在至少一个颜色通道中的强度非常低。利用这一先验和雾霾成像模型,我们可以直接估计雾霾的厚度,并恢复出高质量的无雾霾图像。对各种雾霾图像的实验结果表明了所提出的先验的有效性。此外,去除雾霾还可以作为副产品得到高质量的深度图。
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