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

ABSTRACT: 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.
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.
Realistic Large-Scale Fine-Depth Dehazing Dataset from 3D Videos
http://arxiv.org/abs/2004.08554v3