  In dense foggy scenes, existing optical flow methods are erroneous. This is
due to the degradation caused by dense fog particles that break the optical
flow basic assumptions such as brightness and gradient constancy. To address
the problem, we introduce a semi-supervised deep learning technique that
employs real fog images without optical flow ground-truths in the training
process. Our network integrates the domain transformation and optical flow
networks in one framework. Initially, given a pair of synthetic fog images, its
corresponding clean images and optical flow ground-truths, in one training
batch we train our network in a supervised manner. Subsequently, given a pair
of real fog images and a pair of clean images that are not corresponding to
each other (unpaired), in the next training batch, we train our network in an
unsupervised manner. We then alternate the training of synthetic and real data
iteratively. We use real data without ground-truths, since to have
ground-truths in such conditions is intractable, and also to avoid the
overfitting problem of synthetic data training, where the knowledge learned on
synthetic data cannot be generalized to real data testing. Together with the
network architecture design, we propose a new training strategy that combines
supervised synthetic-data training and unsupervised real-data training.
Experimental results show that our method is effective and outperforms the
state-of-the-art methods in estimating optical flow in dense foggy scenes.
