  While the wisdom of training an image dehazing model on synthetic hazy data
can alleviate the difficulty of collecting real-world hazy/clean image pairs,
it brings the well-known domain shift problem. From a different yet new
perspective, this paper explores contrastive learning with an adversarial
training effort to leverage unpaired real-world hazy and clean images, thus
bridging the gap between synthetic and real-world haze is avoided. We propose
an effective unsupervised contrastive learning paradigm for image dehazing,
dubbed UCL-Dehaze. Unpaired real-world clean and hazy images are easily
captured, and will serve as the important positive and negative samples
respectively when training our UCL-Dehaze network. To train the network more
effectively, we formulate a new self-contrastive perceptual loss function,
which encourages the restored images to approach the positive samples and keep
away from the negative samples in the embedding space. Besides the overall
network architecture of UCL-Dehaze, adversarial training is utilized to align
the distributions between the positive samples and the dehazed images. Compared
with recent image dehazing works, UCL-Dehaze does not require paired data
during training and utilizes unpaired positive/negative data to better enhance
the dehazing performance. We conduct comprehensive experiments to evaluate our
UCL-Dehaze and demonstrate its superiority over the state-of-the-arts, even
only 1,800 unpaired real-world images are used to train our network. Source
code has been available at https://github.com/yz-wang/UCL-Dehaze.

Computer Vision and Pattern Recognition