Robust Unpaired Image Dehazing via Density and Depth Decomposition

To overcome the overfitting issue of dehazing models trained on synthetic hazy-clean image pairs, recent methods attempt to boost the generalization ability by training on unpaired data. However, most of existing approaches simply resort to formulating dehazing–rehazing cycles with generative adversarial networks, yet ignore the physical property in the real-world hazy environment, i.e., the haze effect varies along with density and depth. This paper proposes a robust self-augmented image dehazing framework for haze generation and removal. Instead of merely estimating transmission maps or clean content, the proposed scheme focuses on exploring the scattering coefficient and depth information of hazy and clean images. Having the scene depth estimated, our method is capable of re-rendering hazy images with different thicknesses, which benefits the training of the dehazing network. Besides, a dual contrastive perceptual loss is introduced to further improve the quality of both dehazed and rehazed images. Comprehensive experiments are conducted to reveal the advance of our method over other state-of-the-art unpaired dehazing methods in terms of visual quality, model size, and computational cost. Moreover, our model can be robustly trained on, not only synthetic indoor datasets, but also real outdoor scenes with remarkable improvement on the real-world image dehazing. Our code and training data are available at: https://github.com/YaN9-Y/D4_plus.