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Due to the lack of natural scene and haze prior information, it is greatlychallenging to completely remove the haze from a single image withoutdistorting its visual content. Fortunately, the real-world haze usuallypresents non-homogeneous distribution, which provides us with many valuableclues in partial well-preserved regions. In this paper, we propose aNon-Homogeneous Haze Removal Network (NHRN) via artificial scene prior andbidimensional graph reasoning. Firstly, we employ the gamma correctioniteratively to simulate artificial multiple shots under different exposureconditions, whose haze degrees are different and enrich the underlying sceneprior. Secondly, beyond utilizing the local neighboring relationship, we builda bidimensional graph reasoning module to conduct non-local filtering in thespatial and channel dimensions of feature maps, which models their long-rangedependency and propagates the natural scene prior between the well-preservednodes and the nodes contaminated by haze. To the best of our knowledge, this isthe first exploration to remove non-homogeneous haze via the graph reasoningbased framework. We evaluate our method on different benchmark datasets. Theresults demonstrate that our method achieves superior performance over manystate-of-the-art algorithms for both the single image dehazing and hazy imageunderstanding tasks. The source code of the proposed NHRN is available onhttps://github.com/whrws/NHRNet.

Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning