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Image dehazing is one of the important and popular topics in computer visionand machine learning. A reliable real-time dehazing method with reliableperformance is highly desired for many applications such as autonomous driving,security surveillance, etc. While recent learning-based methods requiredatasets containing pairs of hazy images and clean ground truth, it isimpossible to capture them in real scenes. Many existing works compromise thisdifficulty to generate hazy images by rendering the haze from depth on commonRGBD datasets using the haze imaging model. However, there is still a gapbetween the synthetic datasets and real hazy images as large datasets withhigh-quality depth are mostly indoor and depth maps for outdoor are imprecise.In this paper, we complement the existing datasets with a new, large, anddiverse dehazing dataset containing real outdoor scenes from High-Definition(HD) 3D movies. We select a large number of high-quality frames of real outdoorscenes and render haze on them using depth from stereo. Our dataset is clearlymore realistic and more diversified with better visual quality than existingones. More importantly, we demonstrate that using this dataset greatly improvesthe dehazing performance on real scenes. In addition to the dataset, we alsoevaluate a series state of the art methods on the proposed benchmarkingdatasets.

Realistic Large-Scale Fine-Depth Dehazing Dataset from 3D Videos