  The design of deep learning methods for low light video enhancement remains a
challenging problem owing to the difficulty in capturing low light and ground
truth video pairs. This is particularly hard in the context of dynamic scenes
or moving cameras where a long exposure ground truth cannot be captured. We
approach this problem by training a model on static videos such that the model
can generalize to dynamic videos. Existing methods adopting this approach
operate frame by frame and do not exploit the relationships among neighbouring
frames. We overcome this limitation through a selfcross dilated attention
module that can effectively learn to use information from neighbouring frames
even when dynamics between the frames are different during training and test
times. We validate our approach through experiments on multiple datasets and
show that our method outperforms other state-of-the-art video enhancement
algorithms when trained only on static videos.
