1 research outputs found
Learning to Dehaze from Realistic Scene with A Fast Physics-based Dehazing Network
Dehazing is a popular computer vision topic for long. A real-time dehazing
method with reliable performance is highly desired for many applications such
as autonomous driving. While recent learning-based methods require datasets
containing pairs of hazy images and clean ground truth references, it is
generally impossible to capture accurate ground truth 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 more realistic than existing ones and we
demonstrate that using this dataset greatly improves the dehazing performance
on real scenes. In addition to the dataset, we also propose a light and
reliable dehazing network inspired by the physics model. Our approach
outperforms other methods by a large margin and becomes the new
state-of-the-art method. Moreover, the light-weight design of the network
enables our method to run at a real-time speed, which is much faster than other
baseline methods