  Caused by the difference of data distributions, intra-domain gap and
inter-domain gap are widely present in image processing tasks. In the field of
image dehazing, certain 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 source domain to the target domain
without taking into account the large distribution shift within the target
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.
