  Deep learning-based source dehazing methods trained on synthetic datasets
have achieved remarkable performance but suffer from dramatic performance
degradation on real hazy images due to domain shift. Although certain Domain
Adaptation (DA) dehazing methods have been presented, they inevitably require
access to the source dataset to reduce the gap between the source synthetic and
target real domains. To address these issues, we present a novel Source-Free
Unsupervised Domain Adaptation (SFUDA) image dehazing paradigm, in which only a
well-trained source model and an unlabeled target real hazy dataset are
available. Specifically, we devise the Domain Representation Normalization
(DRN) module to make the representation of real hazy domain features match that
of the synthetic domain to bridge the gaps. With our plug-and-play DRN module,
unlabeled real hazy images can adapt existing well-trained source networks.
Besides, the unsupervised losses are applied to guide the learning of the DRN
module, which consists of frequency losses and physical prior losses. Frequency
losses provide structure and style constraints, while the prior loss explores
the inherent statistic property of haze-free images. Equipped with our DRN
module and unsupervised loss, existing source dehazing models are able to
dehaze unlabeled real hazy images. Extensive experiments on multiple baselines
demonstrate the validity and superiority of our method visually and
quantitatively.

Computer Vision and Pattern Recognition;Artificial Intelligence