  To solve the issue of video dehazing, there are two main tasks to attain: how
to align adjacent frames to the reference frame; how to restore the reference
frame. Some papers adopt explicit approaches (e.g., the Markov random field,
optical flow, deformable convolution, 3D convolution) to align neighboring
frames with the reference frame in feature space or image space, they then use
various restoration methods to achieve the final dehazing results. In this
paper, we propose a progressive alignment and restoration method for video
dehazing. The alignment process aligns consecutive neighboring frames stage by
stage without using the optical flow estimation. The restoration process is not
only implemented under the alignment process but also uses a refinement network
to improve the dehazing performance of the whole network. The proposed networks
include four fusion networks and one refinement network. To decrease the
parameters of networks, three fusion networks in the first fusion stage share
the same parameters. Extensive experiments demonstrate that the proposed video
dehazing method achieves outstanding performance against the-state-of-art
methods.
