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.

翻译：考虑到单图像去雾的病态特性，对比正则化已被引入，通过引入负样本图像作为下界来约束解空间。然而，对比样本缺乏共识性，因为负样本通常被表征为远离清晰（即正样本）图像，导致解空间仍然欠约束。此外，深度去雾模型对雾化过程物理机制的可解释性尚未充分探索。本文提出一种新型课程对比正则化方法，旨在构建共识性对比空间而非非共识性对比空间。我们的负样本通过以下方式组合：1）有雾图像，2）其他现有方法对应的复原结果，从而提供更优的下界约束。由于清晰图像与负样本嵌入之间的相似性存在差异，多组分的训练难度自然呈现不均衡性。为解决该问题，我们定制了课程学习策略以重新加权不同负样本的重要性。此外，为提升特征空间的可解释性，我们根据大气散射模型构建了物理感知双分支单元。结合该单元与课程对比正则化，我们建立了名为C2PNet的去雾网络。大量实验表明，我们的C2PNet显著优于现有最先进方法，在SOTS室内和SOTS室外数据集上分别实现了3.94dB和1.50dB的PSNR极大提升。