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Caused by the difference of data distributions, intra-domain gap andinter-domain gap are widely present in image processing tasks. In the field ofimage dehazing, certain previous works have paid attention to the inter-domaingap between the synthetic domain and the real domain. However, those methodsonly establish the connection from the source domain to the target domainwithout taking into account the large distribution shift within the targetdomain (intra-domain gap). In this work, we propose a Two-Step Dehazing Network(TSDN) with an intra-domain adaptation and a constrained inter-domainadaptation. First, we subdivide the distributions within the synthetic domaininto subsets and mine the optimal subset (easy samples) by loss-basedsupervision. To alleviate the intra-domain gap of the synthetic domain, wepropose an intra-domain adaptation to align distributions of other subsets tothe optimal subset by adversarial learning. Finally, we conduct the constrainedinter-domain adaptation from the real domain to the optimal subset of thesynthetic domain, alleviating the domain shift between domains as well as thedistribution shift within the real domain. Extensive experimental resultsdemonstrate that our framework performs favorably against the state-of-the-artalgorithms both on the synthetic datasets and the real datasets.

Two-Step Image Dehazing with Intra-domain and Inter-domain Adaptation