Two-step image dehazing with intra-domain and inter-domain adaptation

Intra-domain and inter-domain gaps are widely presented in image processing tasks due to data distribution differences. In the field of image dehazing, particular previous works have paid attention to the inter-domain gap between the synthetic domain and the real domain. However, those methods only establish the connection from the real domain to the synthetic domain without considering the significant distribution shift within the synthetic domain (intra-domain gap). In this work, we propose a Two-Step Dehazing Network (TSDN) with an intra-domain adaptation and a constrained inter-domain adaptation. First, we subdivide the distributions within the synthetic domain into subsets and mine the optimal subset (easy samples) by loss-based supervision. To alleviate the intra-domain gap of the synthetic domain, we propose an intra-domain adaptation to align distributions of other subsets to the optimal subset by adversarial learning. Finally, we conduct the constrained inter-domain adaptation from the real domain to the optimal subset of the synthetic domain, alleviating the domain shift between domains as well as the distribution shift within the real domain. Extensive experimental results demonstrate that our framework performs favorably against the state-of-the-art algorithms both on the synthetic datasets and the real datasets.