  Deep learning-based methods have made significant achievements for image
dehazing. However, most of existing dehazing networks are concentrated on
training models using simulated hazy images, resulting in generalization
performance degradation when applied on real-world hazy images because of
domain shift. In this paper, we propose a mutual learning dehazing framework
for domain adaption. Specifically, we first devise two siamese networks: a
teacher network in the synthetic domain and a student network in the real
domain, and then optimize them in a mutual learning manner by leveraging EMA
and joint loss. Moreover, we design a sample-cycle strategy based on density
augmentation (HDA) module to introduce pseudo real-world image pairs provided
by the student network into training for further improving the generalization
performance. Extensive experiments on both synthetic and real-world dataset
demonstrate that the propose mutual learning framework outperforms
state-of-the-art dehazing techniques in terms of subjective and objective
evaluation.
