Physically Disentangled Intra- and Inter-Domain Adaptation for Varicolored Haze Removal

Yi Li, Yi Chang, Yan Gao, Changfeng Yu, Luxin Yan; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 5841-5850

Learning-based image dehazing methods have achieved marvelous progress during the past few years. On one hand, most approaches heavily rely on synthetic data and may face difficulties to generalize well in real scenes, due to the huge domain gap between synthetic and real images. On the other hand, very few works have considered the varicolored haze, caused by chromatic casts in real scenes. In this work, our goal is to handle the new task: real-world varicolored haze removal. To this end, we propose a physically disentangled joint intra- and inter-domain adaptation paradigm, in which intra-domain adaptation focuses on color correction and inter-domain procedure transfers knowledge between synthetic and real domains. We first learn to physically disentangle haze images into three components complying with the scattering model: background, transmission map, and atmospheric light. Since haze color is determined by atmospheric light, we perform intra-domain adaptation by specifically translating atmospheric light from varicolored space to unified color-balanced space, and then reconstructing color-balanced haze image through the scattering model. Consequently, we perform inter-domain adaptation between the synthetic and real images by mutually exchanging the background and other two components. Then we can reconstruct both identity and domain-translated haze images with self-consistency and adversarial loss. Extensive experiments demonstrate the superiority of the proposed method over the state-of-the-art for real varicolored image dehazing.