Unpaired Image Dehazing With Physical-Guided Restoration and Depth-Guided Refinement

Most existing single image dehazing methods aim to learn supervised models from paired synthetic data, which often limits their generalization ability in real-world applications. Besides, due to ignoring the merits of physical model in visibility restoration and the properties of depth features in clarity improvement, we observe that only relying on the transfer capability of unpaired adversarial learning will suffer from low-quality recovery. To this end, we develop an effective end-to-end unpaired image dehazing method by integrating a physical-guided restoration stage and a depth-guided refinement stage in a GAN framework, named as PDR-GAN. Specifically, the dark channel prior is embedded in the restoration stage to provide constraints for the network, and the preliminary dehazed image is first generated. For the refinement stage, we excavate the potential relationship between the depth and transmission map to better refine the results of the previous stage and further recover the distant area details. Our framework benefits from the stage-wise learning strategy of model-based restoration and feature-based reconstruction, which is especially helpful for image dehazing when paired data is not available. Experimental results show that our method is superior to the current unpaired dehazing approaches in terms of both quantitative and qualitative.