  Considering the ill-posed nature, contrastive regularization has been
developed for single image dehazing, introducing the information from negative
images as a lower bound. However, the contrastive samples are nonconsensual, as
the negatives are usually represented distantly from the clear (i.e., positive)
image, leaving the solution space still under-constricted. Moreover, the
interpretability of deep dehazing models is underexplored towards the physics
of the hazing process. In this paper, we propose a novel curricular contrastive
regularization targeted at a consensual contrastive space as opposed to a
non-consensual one. Our negatives, which provide better lower-bound
constraints, can be assembled from 1) the hazy image, and 2) corresponding
restorations by other existing methods. Further, due to the different
similarities between the embeddings of the clear image and negatives, the
learning difficulty of the multiple components is intrinsically imbalanced. To
tackle this issue, we customize a curriculum learning strategy to reweight the
importance of different negatives. In addition, to improve the interpretability
in the feature space, we build a physics-aware dual-branch unit according to
the atmospheric scattering model. With the unit, as well as curricular
contrastive regularization, we establish our dehazing network, named C2PNet.
Extensive experiments demonstrate that our C2PNet significantly outperforms
state-of-the-art methods, with extreme PSNR boosts of 3.94dB and 1.50dB,
respectively, on SOTS-indoor and SOTS-outdoor datasets.
