Image dehazing is one of the important and popular topics in computer vision and machine learning. A reliable real-time dehazing method with reliable performance is highly desired for many applications such as autonomous driving, security surveillance, etc. While recent learning-based methods require datasets containing pairs of hazy images and clean ground truth, it is impossible to capture them in real scenes. Many existing works compromise this difficulty to generate hazy images by rendering the haze from depth on common RGBD datasets using the haze imaging model. However, there is still a gap between the synthetic datasets and real hazy images as large datasets with high-quality depth are mostly indoor and depth maps for outdoor are imprecise. In this paper, we complement the existing datasets with a new, large, and diverse dehazing dataset containing real outdoor scenes from High-Definition (HD) 3D movies. We select a large number of high-quality frames of real outdoor scenes and render haze on them using depth from stereo. Our dataset is clearly more realistic and more diversified with better visual quality than existing ones. More importantly, we demonstrate that using this dataset greatly improves the dehazing performance on real scenes. In addition to the dataset, we also evaluate a series state of the art methods on the proposed benchmarking datasets.
翻译:图像脱色是计算机视觉和机器学习中重要和流行的主题之一。对于自主驾驶、安全监视等许多应用程序来说,可靠的实时脱色方法非常需要可靠且性能可靠的脱色方法。尽管最近的基于学习的方法要求数据集包含一对模糊的图像和干净的地面真相,但不可能在真实的场景中捕捉它们。许多现有的工作通过利用烟雾成像模型在共同的 RGBD 数据集中从深度上将烟雾化,从而抵消了生成模糊图像的难度。然而,合成数据集与真实的隐蔽图像之间仍然存在着差距,因为高品质深度的大型数据集大多是室内和室外深度的地图。在本文中,我们用新的、大型和多样的脱色数据集来补充现有数据集,其中包括高精度(HD) 3D 电影中真实的室外场景。我们选择了大量高品质的室外场景框架,并用更深的立体深度来遮盖这些图像。我们的数据集显然更现实,而且更加多样化,而且比现有更清晰的视觉质量更高。更重要的是,我们用一个真实的图像模型来评估我们所提出的一系列数据。我们如何改进了数据。