Single Image Dehazing via Semi-Supervised Domain Translation and Architecture Search

Data-driven methods have demonstrated great potential in single image dehazing, most of which are trained using the hazy images synthesized by the atmospheric scattering model. However, the model cannot accurately describe the complicated degradation caused by haze. At the same time, it is impractical to obtain the pixel-to-pixel aligned clear-scene counterparts of real-world hazy images for supervised training. In this letter, we formulate dehazing as a semi-supervised domain translation problem. For better generalization, two auxiliary domain translation tasks are designed to capture the properties of real-world haze and align synthetic hazy images to real-world ones to reduce the domain gap. Dehazing and the auxiliary tasks are conducted in shared latent spaces by a unified framework, and we use differential optimization to search the architectures of the framework. We evaluate the efficacy of the proposed work using one synthetic and three real-world benchmarks that cover the challenging cases in wild scenarios, and it outperforms state-of-the-art algorithms on these benchmarks. The benefits brought by auxiliary domain translation tasks and architecture search are also verified by ablation experiments.