Recent research have shown the potential of using

feed-forward convolutional neural networks to accomplish single

image dehazing. In this work, we take one step further to

explore the possibility of exploiting a feed-forward network to

perform haze removal for videos. Unlike single image dehazing,

video based approaches can take advantage of the abundant

information that exists across neighboring frames. In this work,

assuming that a scene point yields highly correlated transmission

values between adjacent video frames, we develop a deep learning

solution for video dehazing, where a CNN is trained end-toend

to learn how to accumulate information across frames

for transmission estimation. The estimated transmission map is

subsequently used to recover a haze-free frame via atmospheric

scattering model. In addition, as the semantic information of a

scene provides a strong prior for image restoration, we propose

to incorporate global semantic priors as input to regularize the

transmission maps so that the estimated maps can be smooth

in the regions of the same object and only discontinuous across

the boundaries of different objects. To train this network, we

generate a dataset consisted of synthetic hazy and haze-free

videos for supervision based on the NYU depth dataset. We

show that the features learned from this dataset are capable

of removing haze that arises in outdoor scenes in a wide

range of videos. Extensive experiments demonstrate that the

proposed algorithm performs favorably against the state-of-theart

methods on both synthetic and real-world videos.